how has lack of testing availability led to underreporting of true incidence of Covid-19?

To understand the scope and development of the COVID-19 pandemic, knowledge of the number of infected persons is essential. Often, the number of "confirmed cases", which is based on positive RT-PCR test results, is regarded as a reasonable indicator. However, limited COVID-19 test capacities in many countries are restricting the amount of testing that can be done. This can lead to the implementation of testing policies that restrict access to COVID-19 tests, and to testing backlogs and delays. As a result, confirmed case numbers can be significantly lower than the actual number of infections, especially during rapid growth phases of the epidemic. This study examines the quantitative relation between infections and reported confirmed case numbers for two different testing strategies, "limited" and "inclusive" testing, in relation to the growth rate of the epidemic. The results indicate that confirmed case numbers understate the actual number of infections substantially; during rapid growth phases where the daily growth rate can reach or exceed 30%, as has been seen in many countries, the confirmed case numbers under-report actual infections by up to 50 to 100-fold.

Severe underestimation of COVID-19 case numbers: effect of epidemic growth rate and test restrictions

Abstract Since the novel coronavirus disease (COVID-19) emerged in December 2019 in China, it has rapidly propagated to around the world, leading to one of the most significant pandemic events of recent history. Deriving reliable estimates of the COVID-19 epidemic growth rate is quite important to guide the timing and intensity of intervention strategies. Indeed, many studies have quantified the epidemic growth rate using time-series of reported cases during the early phase of the outbreak to estimate the basic reproduction number, R 0. Using daily time series of COVID-19 incidence, we illustrate how epidemic curves of reported cases may not always reflect the true epidemic growth rate due to changes in testing rates, which could be influenced by limited diagnostic testing capacity during the early epidemic phase.

Changes in testing rates could mask the novel coronavirus disease (COVID-19) growth rate

Since the novel coronavirus disease (COVID-19) emerged in December 2019 in China, it has rapidly spread around the world, leading to one of the most significant pandemic events of recent history. Deriving reliable estimates of the COVID-19 epidemic growth rate is quite important to guide the timing and intensity of intervention strategies. Indeed, many studies have quantified the epidemic growth rate using time-series of reported cases during the early phase of the outbreak to estimate the basic reproduction number, R0. Using daily time series of COVID-19 incidence, we illustrate how epidemic curves of reported cases may not always reflect the true epidemic growth rate due to changes in testing rates, which could be influenced by limited diagnostic testing capacity during the early epidemic phase.

To design effective disease control strategies, it is critical to understand the incidence of diseases. In the Covid-19 epidemic in the United States (caused by outbreak of the SARS-CoV-2 virus), testing capacity was initially very limited and has been increasing at the same time as the virus has been spreading. When estimating the incidence, it can be difficult to distinguish whether increased numbers of positive tests stem from increases in the spread of the virus or increases in testing. This has made it very difficult to identify locations in which the epidemic poses the largest public health risks. Here, we use a probabilistic model to quantify beliefs about testing strategies and understand implications regarding incidence. We apply this model to estimate the incidence in each state of the United States, and find that: (1) the Covid-19 epidemic is likely to be more widespread than reported by limited testing, (2) the Covid-19 epidemic growth in the summer months is likely smaller than it was during the spring months, and (3) the regions which are at highest risk of Covid-19 epidemic outbreaks are not always those with the largest number of positive test results.

Disentangling Increased Testing From Covid-19 Epidemic Spread

Accurate estimates of the burden of SARS-CoV-2 infection are critical to informing pandemic response. Current confirmed COVID-19 case counts in the U.S. do not capture the total burden of the pandemic because testing has been primarily restricted to individuals with moderate to severe symptoms due to limited test availability. Using a semi-Bayesian probabilistic bias analysis to account for incomplete testing and imperfect diagnostic accuracy, we estimated 6,275,072 cumulative infections compared to 721,245 confirmed cases (1.9% vs. 0.2% of the population) as of April 18, 2020. Accounting for uncertainty, the number of infections was 3 to 20 times higher than the number of confirmed cases. 86% (simulation interval: 64-99%) of this difference was due to incomplete testing, while 14% (0.3-36%) was due to imperfect test accuracy. Estimates of SARS-CoV-2 infections that transparently account for testing practices and diagnostic accuracy reveal that the pandemic is larger than confirmed case counts suggest.

Substantial underestimation of SARS-CoV-2 infection in the United States due to incomplete testing and imperfect test accuracy

BACKGROUND: Under the unique Japanese policy to restrict reverse transcriptase-polymerase chain reaction (RT-PCR) testing against severe acute respiratory syndrome coronavirus 2, a nationwide number of its confirmed cases and mortality remains to be low. Yet the information is lacking on geographical differences of these measures and their associated factors. AIM: Evaluation of prefecture-based geographical differences and associated predictors for the incidence and number of RT-PCR tests for COVID-19. DESIGN: Cross-sectional study using regression and correlation analysis. METHODS: We retrieved domestic laboratory-confirmed cases, deaths, and the number of RT-PCR testing for COVID-19 from January 15 to April 6, 2020 in 47 prefectures in Japan, using publicly-available data by the Ministry of Health, Labour and Welfare. We did descriptive analyses of these three measures and identified significant predictors for the incidence and RT-PCR testing through multiple regression analyses and correlates with the number of deaths through correlation analysis. RESULTS: The median prefectural-level incidence and number of RT-PCR testing per 100,000 population were 1.14 and 38.6, respectively. Multiple regression analyses revealed that significant predictors for the incidence were prefectural-level population (p < 0.001) and the number of RT-PCR testing (p = 0.03); and those for RT-PCR testing were the incidence (p = 0.025), available beds (p = 0.045) and cluster infections (p = 0.034). CONCLUSION: Considering bidirectional association between the incidence and RT-PCR testing, there may have been an underdiagnosed population for the infection. The restraint policy for RT-PCR testing should be revisited to meet the increasing demand under the COVID-19 epidemic.

Underestimation of COVID-19 cases in Japan: an analysis of RT-PCR testing for COVID-19 among 47 prefectures in Japan

Many statisticians, epidemiologists, economists and data scientists have registered serious reservations regarding the reported coronavirus case-counts. Limited testing capacity across the country has been widely identified as a key driver of suppressed coronavirus case-counts. The calls to increase testing capacity are well-justified as they become a more frequent point of discussion in the public sphere. While expanded testing is a laudable goal, selection bias will impact estimates of disease prevalence and the effective reproduction number until the entire population is sampled. Moreover, tests are imperfect as false positive/negative rates interact in complex ways with selection bias. In this paper, we attempt to clarify this interaction. Through simple calculations, we demonstrate pitfalls and paradoxes that can arise when considering case-count data in the presence of selection bias and measurement error. The discussion guides several suggestions on how to improve current case-count reporting.

The Hypothesis of Testing: Paradoxes arising out of reported coronavirus case-counts

BACKGROUND Under the unique Japanese policy to restrict reverse transcriptase-polymerase chain reaction (RT-PCR) testing against severe acute respiratory syndrome coronavirus 2, a nationwide number of its confirmed cases and mortality remains to be low. Yet the information is lacking on geographical differences of these measures and their associated factors. AIM Evaluation of prefecture-based geographical differences and associated predictors for the incidence and number of RT-PCR tests for COVID-19. DESIGN Cross-sectional study using regression and correlation analysis. METHODS We retrieved domestic laboratory-confirmed cases, deaths, and the number of RT-PCR testing for COVID-19 from January 15 to April 6, 2020 in 47 prefectures in Japan, using publicly-available data by the Ministry of Health, Labour and Welfare. We did descriptive analyses of these three measures and identified significant predictors for the incidence and RT-PCR testing through multiple regression analyses and correlates with the number of deaths through correlation analysis. RESULTS The median prefectural-level incidence and number of RT-PCR testing per 100,000 population were 1.14 and 38.6, respectively. Multiple regression analyses revealed that significant predictors for the incidence were prefectural-level population (p < 0.001) and the number of RT-PCR testing (p = 0.03); and those for RT-PCR testing were the incidence (p = 0.025), available beds (p = 0.045) and cluster infections (p = 0.034). CONCLUSION Considering bidirectional association between the incidence and RT-PCR testing, there may have been an underdiagnosed population for the infection. The restraint policy for RT-PCR testing should be revisited to meet the increasing demand under the COVID-19 epidemic.

Underestimation of COVID-19 cases in Japan: an analysis of RT-PCR testing for COVID-19 among 47 prefectures in Japan.

Estimates of the reproductive number for novel pathogens such as SARS-CoV-2 are essential for understanding the potential trajectory of the epidemic and the level of intervention that is needed to bring the epidemic under control. However, most methods for estimating the basic reproductive number (R(0)) and time-varying effective reproductive number (R(t)) assume that the fraction of cases detected and reported is constant through time. We explore the impact of secular changes in diagnostic testing and reporting on estimates of R(0) and R(t) using simulated data. We then compare these patterns to data on reported cases of COVID-19 and testing practices from different United States (US) states. We find that changes in testing practices and delays in reporting can result in biased estimates of R(0) and R(t). Examination of changes in the daily number of tests conducted and the percent of patients testing positive may be helpful for identifying the potential direction of bias. Changes in diagnostic testing and reporting processes should be monitored and taken into consideration when interpreting estimates of the reproductive number of COVID-19.

The impact of changes in diagnostic testing practices on estimates of COVID-19 transmission in the United States

Standard measures such as quarantining suspects and contact tracing could not contain the ongoing COVID-19 pandemic. Unlike for other recent epidemics where such instruments were successful, in the present case a large fraction of the infected have only mild unspecific symptoms. By employing network models we here show that even for near perfect contact tracing and unlimited suspect testing, containment starts to fail when more than approximately half of the carriers fall into the weak-symptom category. The situation becomes considerably more severe if the number of daily available tests is limited. In this case the epidemic transition becomes discontinuous and a much larger percentage of the population becomes infected. While moderate levels of social distancing can bring the situation back under control, the limited number of daily tests introduces a finite time horizon: If social distancing is implemented after the cut off date, containment catastrophically breaks down resulting in an exponential disease spread.

Catastrophic failure of outbreak containment: Limited testing causes discontinuity in epidemic transition

The Coronavirus disease 2019(COVID-19) outbreak has caused havoc across the world. Subsequently, research on COVID-19 has focused on number of cases and deaths and predicted projections have focused on these parameters. We propose that the number of tests performed is a very important denominator in understanding the COVID-19 data. We analysed the number of diagnostic tests performed in proportion to the number of cases and subsequently deaths across different countries and projected pandemic outcomes. We obtained real time COVID-19 data from the reference website Worldometer at 0900 BST on Saturday 4th April, 2020 and collated the information obtained on the top 50 countries with the highest number of COVID 19 cases. We analysed this data according to the number of tests performed as the main denominator. Country wise population level pandemic projections were extrapolated utilising three models - 1) inherent case per test and death per test rates at the time of obtaining the data (4/4/2020 0900 BST) for each country; 2) rates adjusted according to the countries who conducted at least 100000 tests and 3) rates adjusted according to South Korea. We showed that testing rates impact on the number of cases and deaths and ultimately on future projections for the pandemic across different countries. We found that countries with the highest testing rates per population have the lowest death rates and give us an early indication of an eventual COVID-19 mortality rate. It is only by continued testing on a large scale that will enable us to know if the increasing number of patients who are seriously unwell in hospitals across the world are the tip of the iceberg or not. Accordingly, obtaining this information through a rapid increase in testing globally is the only way which will enable us to exit the COVID-19 pandemic and reduce economic and social instability.

Making sense of the Global Coronavirus Data: The role of testing rates in understanding the pandemic and our exit strategy

Abstract Background With continuous global COVID-19 outbreak, differing case numbers and mortality rates are observed. While actual case numbers appear vague, mortality numbers related to COVID-19 seem more precise. In this study, we used the mortality rate as the main indicator to evaluate the extent of underreporting and underdetection of COVID-19 cases. Methods We have analyzed all available data provided by the World Health Organization on the development of international COVID-19 cases and mortality numbers on March 17th, 2020. A crude case-fatality risk (cCFR) and adjusted case-fatality risk (aCFR) was calculated for China, South Korea, Japan, Italy, France, Spain, Germany, Iran and the United States. Additionally, a fold-change (FC) was derived for each country. Results The highest aCFR and FC were detected for Spain. Based on their FC values, an extremely high number of undetected COVID-19 cases was displayed in France, the United States, Italy and Spain. For these countries, our findings indicate a detection rate of only 1-2% of total actual COVID-19 cases. Conclusions Due to limited testing capacities, mortality numbers may serve as a better indicator for COVID-19 case spread in many countries. Our data indicate that countries like France, Italy, the United States, Iran and Spain have extremely high numbers of undetected and underreported cases. Differences in testing availability and capacity, containment as well as overall health care and medical infrastructure result in significantly different mortality rates and COVID-19 case numbers for each respective country.

Evaluating the massive underreporting and undertesting of COVID-19 cases in multiple global epicenters

BACKGROUND: With continuous global COVID-19 outbreak, differing case numbers and mortality rates are observed. While actual case numbers appear vague, mortality numbers related to COVID-19 seem more precise. In this study, we used the mortality rate as the main indicator to evaluate the extent of underreporting and underdetection of COVID-19 cases. METHODS: We have analyzed all available data provided by the World Health Organization on the development of international COVID-19 cases and mortality numbers on March 17th, 2020. A crude case-fatality risk (cCFR) and adjusted case-fatality risk (aCFR) was calculated for China, South Korea, Japan, Italy, France, Spain, Germany, Iran and the United States. Additionally, a fold-change (FC) was derived for each country. RESULTS: The highest aCFR and FC were detected for Spain. Based on their FC values, an extremely high number of undetected COVID-19 cases was displayed in France, the United States, Italy and Spain. For these countries, our findings indicate a detection rate of only 1-2% of total actual COVID-19 cases. CONCLUSIONS: Due to limited testing capacities, mortality numbers may serve as a better indicator for COVID-19 case spread in many countries. Our data indicate that countries like France, Italy, the United States, Iran and Spain have extremely high numbers of undetected and underreported cases. Differences in testing availability and capacity, containment as well as overall health care and medical infrastructure result in significantly different mortality rates and COVID-19 case numbers for each respective country.

We used multi-agent simulations to estimate the testing capacity required to find and isolate a number of infections sufficient to break the chain of transmission of SARS-CoV-2. Depending on the mitigation policies in place, a daily capacity between 0.7 to 3.6 tests per thousand was required to contain the disease. However, if contact tracing and testing efficacy dropped below 60% (e.g. due to false negatives or reduced tracing capability), the number of infections kept growing exponentially, irrespective of any testing capacity. Under these conditions, the population’s geographical distribution and travel behaviour could inform sampling policies to aid a successful containment.

Containment of future waves of COVID-19: simulating the impact of different policies and testing capacities for contact tracing, testing, and isolation

Real-time estimates of the true size and trajectory of local COVID-19 epidemics are key metrics to guide policy responses. We developed a Bayesian nowcasting approach that explicitly accounts for reporting delays and secular changes in case ascertainment to generate real-time estimates of COVID-19 epidemiology on the basis of reported cases and deaths. Using this approach, we estimate time trends in infections, symptomatic cases, and deaths for all 50 US states and the District of Columbia from early-March through June 11, 2020. At the beginning of June, our best estimates of the effective reproduction number (Rt) are close to 1 in most states, indicating a stabilization of incidence, but there is considerable variability in the level of incidence and the estimated proportion of the population that has already been infected.

Bayesian nowcasting with adjustment for delayed and incomplete reporting to estimate COVID-19 infections in the United States

Effectively designing and evaluating public health responses to the ongoing COVID-19 pandemic requires accurate estimation of the weekly incidence of COVID-19. Unfortunately, a lack of systematic testing across the United States (US) due to equipment shortages and varying testing strategies has hindered the usefulness of the reported positive COVID-19 case counts. We introduce three complementary approaches to estimate the cumulative incidence of symptomatic COVID-19 during the early outbreak in each state in the US as well as in New York City, using a combination of excess influenza-like illness reports, COVID-19 test statistics, and COVID-19 mortality reports. Instead of relying on an estimate from a single data source or method that may be biased, we provide multiple estimates, each relying on different assumptions and data sources. Across our three approaches, there is a consistent conclusion that estimated state-level COVID-19 symptomatic case counts from March 1 to April 4, 2020 varied from 5 to 50 times greater than the official positive test counts. Nationally, our estimates of COVID-19 symptomatic cases in the US as of April 4 have a likely range of 2.2 to 5.1 million cases, with possibly as high as 8.1 million cases, up to 26 times greater than the cumulative confirmed cases of about 311,000. Extending our method to May 16, 2020, we estimate that cumulative symptomatic incidence ranges from 6.0 to 12.2 million, which compares with 1.5 million positive test counts. Our approaches demonstrate the value of leveraging existing influenza-like-illness surveillance systems during the flu season for measuring the burden of new diseases that share symptoms with influenza-like-illnesses. Our methods may prove useful in assessing the burden of COVID-19 during upcoming flu seasons in the US and other countries with comparable influenza surveillance systems.

Estimating the Early Outbreak Cumulative Incidence of COVID-19 in the United States: Three Complementary Approaches

The new corona virus disease -- COVID-2019 -- is rapidly spreading through the world. The availability of unbiased timely statistics of trends in disease events are a key to effective responses. But due to reporting delays, the most recently reported numbers are frequently underestimating of the total number of infections, hospitalizations and deaths creating an illusion of a downward trend. Here we describe a statistical methodology for predicting true daily quantities and their uncertainty, estimated using historical reporting delays. The methodology takes into account the observed distribution pattern of the lag. It is derived from the removal method, a well-established estimation framework in the field of ecology.

Nowcasting Covid-19 statistics reported withdelay: a case-study of Sweden

The COVID-19 virus has spread worldwide in a matter of a few months, while healthcare systems struggle to monitor and report current cases. Testing results have struggled with the relative capabilities, testing policies and preparedness of each affected country, making their comparison a non-trivial task. Since severe cases, which more likely lead to fatal outcomes, are detected at a higher rate than mild cases, the reported virus mortality is likely inflated in most countries. Lockdowns and changes in human behavior modulate the underlying growth rate of the virus. Under-sampling of infection cases may lead to the under-estimation of total cases, resulting in systematic mortality estimation biases. For healthcare systems worldwide it is important to know the expected number of cases that will need treatment. In this manuscript, we identify a generalizable growth rate decay reflecting behavioral change. We propose a method to correct the reported COVID-19 cases and death numbers by using a benchmark country (South Korea) with near-optimal testing coverage, with considerations on population demographics. We extrapolate expected deaths and hospitalizations with respect to observations in countries that passed the exponential growth curve. By applying our correction, we predict that the number of cases is highly under-reported in most countries and a significant burden on worldwide hospital capacity.

Correcting under-reported COVID-19 case numbers: estimating the true scale of the pandemic

Covid-19: Lack of test and trace data are frustrating government scrutiny.

Accurate forecasts for COVID-19 are necessary for better preparedness and resource management. Specifically, deciding the response over months or several months requires accurate long-term forecasts which is particularly challenging as the model errors accumulate with time. A critical factor that can hinder accurate long-term forecasts, is the number of unreported/asymptomatic cases. While there have been early serology tests to estimate this number, more tests need to be conducted for more reliable results. To identify the number of unreported/asymptomatic cases, we take an epidemiology data-driven approach. We show that we can identify lower bounds on this ratio or upper bound on actual cases as a factor of reported cases. To do so, we propose an extension of our prior heterogeneous infection rate model, incorporating unreported/asymptomatic cases. We prove that the number of unreported cases can be reliably estimated only from a certain time period of the epidemic data. In doing so, we construct an algorithm called Fixed Infection Rate method, which identifies a reliable bound on the learned ratio. We also propose two heuristics to learn this ratio and show their effectiveness on simulated data. We use our approaches to identify the upper bounds on the ratio of actual to reported cases for New York City and several US states. Our results demonstrate with high confidence that the actual number of cases cannot be more than 35 times in New York, 40 times in Illinois, 38 times in Massachusetts and 29 times in New Jersey, than the reported cases.

Data-driven Identification of Number of Unreported Cases for COVID-19: Bounds and Limitations

In May 2020 the UK introduced a Test, Trace, Isolate programme in response to the COVID-19 pandemic. The programme was first rolled out on the Isle of Wight and included Version 1 of the NHS contact tracing app. We used COVID-19 daily case data to infer incidence of new infections and estimate the reproduction number R for each of 150 Upper Tier Local Authorities in England, and at the National level, before and after the launch of the programme on the Isle of Wight. We used Bayesian and Maximum-Likelihood methods to estimate R, and compared the Isle of Wight to other areas using a synthetic control method. We observed significant decreases in incidence and R on the Isle of Wight immediately after the launch. These results are robust across each of our approaches. Our results show that the sub-epidemic on the Isle of Wight was controlled significantly more effectively than the sub-epidemics of most other Upper Tier Local Authorities, changing from having the third highest reproduction number R (of 150) before the intervention to the tenth lowest afterwards. The data is not yet available to establish a causal link. However, the findings highlight the need for further research to determine the causes of this reduction, as these might translate into local and national non-pharmaceutical intervention strategies in the period before a treatment or vaccination becomes available.

COVID-19 incidence and R decreased on the Isle of Wight after the launch of the Test, Trace, Isolate programme

Abstract When the novel coronavirus disease SARS-CoV2 (COVID-19) was officially declared a pandemic by the WHO in March 2020, the scientific community had already braced up in the effort of making sense of the fast-growing wealth of data gathered by national authorities all over the world. However, despite the diversity of novel theoretical approaches and the comprehensiveness of many widely established models, the official figures that recount the course of the outbreak still sketch a largely elusive and intimidating picture. Here we show unambiguously that the dynamics of the COVID-19 outbreak belongs to the simple universality class of the SIR model and extensions thereof. Our analysis naturally leads us to establish that there exists a fundamental limitation to any theoretical approach, namely the unpredictable non-stationarity of the testing frames behind the reported figures. However, we show how such bias can be quantified self-consistently and employed to mine useful and accurate information from the data. In particular, we describe how the time evolution of the reporting rates controls the occurrence of the apparent epidemic peak, which typically follows the true one in countries that were not vigorous enough in their testing at the onset of the outbreak. The importance of testing early and resolutely appears as a natural corollary of our analysis, as countries that tested massively at the start clearly had their true peak earlier and less deaths overall.

COVID-19: The unreasonable effectiveness of simple models

When assessing the relative prevalence of the novel coronavirus (COVID-19), observers often point to the number of COVID-19 cases that have been confirmed through viral testing. However, comparisons based on confirmed case counts alone can be misleading since a higher case count may reflect either a higher disease prevalence or a better rate of disease detection. Using weekly records of viral test results for each state in the US, I demonstrate how confirmed case counts can be adjusted based on the percentage of COVID-19 tests that come back positive. A regression analysis indicates that case counts track better with future hospitalizations and deaths when employing this simple adjustment for testing coverage. Viral testing results can be used as a leading indicator of COVID-19 prevalence, but data reporting standards should be improved, and care should be taken to account for testing coverage when comparing confirmed case counts.

Adjusting confirmed COVID-19 case counts for testing volume

The COVID-19 pandemic has created a public health crisis. Because SARS-CoV-2 can spread from individuals with pre-symptomatic, symptomatic, and asymptomatic infections, the re-opening of societies and the control of virus spread will be facilitated by robust surveillance, for which virus testing will often be central. After infection, individuals undergo a period of incubation during which viral titers are usually too low to detect, followed by an exponential growth of virus, leading to a peak viral load and infectiousness, and ending with declining viral levels and clearance. Given the pattern of viral load kinetics, we model surveillance effectiveness considering test sensitivities, frequency, and sample-to-answer reporting time. These results demonstrate that effective surveillance, including time to first detection and outbreak control, depends largely on frequency of testing and the speed of reporting, and is only marginally improved by high test sensitivity. We therefore conclude that surveillance should prioritize accessibility, frequency, and sample-to-answer time; analytical limits of detection should be secondary.

Test sensitivity is secondary to frequency and turnaround time for COVID-19 surveillance

By March 2020, COVID-19 led to thousands of deaths and disrupted economic activity worldwide. As a result of narrow case definitions and limited capacity for testing, the number of unobserved SARS-CoV-2 infections during its initial invasion of the US remains unknown. We developed an approach for estimating the number of unobserved infections based on data that are commonly available shortly after the emergence of a new infectious disease. The logic of our approach is, in essence, that there are bounds on the amount of exponential growth of new infections that can occur during the first few weeks after imported cases start appearing. Applying that logic to data on imported cases and local deaths in the US through March 12, we estimated that 22,876 (95% posterior predictive interval: 7,451 - 53,044) infections occurred in the US by this date. By comparing the model's predictions of symptomatic infections to local cases reported over time, we obtained daily estimates of the proportion of symptomatic infections detected by surveillance. This revealed that detection of symptomatic infections decreased throughout February as exponential growth of infections outpaced increases in testing. Between February 21 and March 12, we estimated an increase in detection of symptomatic infections, which was strongly correlated (median: 0.97, 95% PPI: 0.85 - 0.98) with increases in testing. These results suggest that testing was a major limiting factor in assessing the extent of SARS-CoV-2 transmission during its initial invasion of the US.

Estimating unobserved SARS-CoV-2 infections in the United States

Background: In the pandemic, testing for SARS-CoV-2 by RT-PCR in one of the pillars on which countermeasures are based. Factors limiting the output of laboratories interfere with the effectiveness of public health measures. Conserving reagents by pooling samples in low-probability settings is proposed, but may cause dilution and loss of sensitivity. Methods: We tested an alternate approach (FACT) by simultaneously incubating multiple respiratory swabs in a single tube. This protocol was evaluated by serial incubation of a respiratory swab in up to 10 tubes. The analytics validity of this concept was demonstrated in a five-sample mini pool set-up. It was consequently applied in the testing of 50 symptomatic patients (five-sample pools) as well as 100 asymptomatic residents of a nursing home (ten-sample pools). Results: Serial incubation of a respiratory swab in up to 10 tubes did not lead to a significant decline in viral concentration. The novel FACT-protocol did not cause a false negative result in a five-sample mini-pool setup, with non-significantly differing Ct values between single sample and mini-pool NAT. In two routine applications, all mini pools containing positive patient samples were correctly identified. Conclusions: Our proposed FACT- protocol did not cause a significant loss in analytic or diagnostic sensitivity compared to single sample testing in multiple setups. It reduced the amount of reagents needed by up to 40%, and also reduced hands-on time. This method could enhance testing efficiency, especially in groups with a low pretest-probability, such as systemically relevant professional groups.

FACT- Frankfurt adjusted COVID-19 testing- a novel method enables high-throughput SARS-CoV-2 screening without loss of sensitivity

SARS-CoV-2 transmission risk generally increases with proximity of those shedding the virus to those susceptible to infection. Thus, this risk is a function of both number of people and the area which they occupy. However, the latter continues to evade COVID-19 testing policy. Increased testing in areas with lower population density, has the potential to induce a false sense of security even as cases continue to rise sharply overall.

COVID-19: Saving lives and livelihoods using population density driven testing

Differences in COVID-19 testing and tracing across countries, as well as changes in testing within each country over time, make it difficult to estimate the true (population) infection rate based on the confirmed number of cases obtained through RNA viral testing. We applied a backcasting approach, coupled with Monte Carlo methods, to estimate a distribution for the true (population) cumulative number of infections (infected and recovered) for 15 countries where reliable data are available. We find a positive relationship between the testing rate per 1,000 people and the implied true detection rate of COVID-19, and a negative relationship between the proportion who test positive and the implied true detection rate. Our estimates suggest that the true number of people infected across our sample of 15 developed countries is 18.2 (5-95% CI: 11.9-39.0) times greater than the reported number of cases. In individual countries, the true number of cases exceeds the reported figure by factors that range from 1.7 (5-95% CI: 1.1-3.6) for Australia to 35.6 (5-95% CI: 23.2-76.3) for Belgium.

Estimating the true (population) infection rate for COVID-19: A Backcasting Approach with Monte Carlo Methods

With the increasing spread of COVID-19, it is important to systematically test more and more people. The current strategy for test-kit allocation is mostly rule-based, focusing on individuals having (a) symptoms for COVID-19, (b) travel history or (c) contact history with confirmed COVID-19 patients. Such testing strategy may miss out on detecting asymptomatic individuals who got infected via community spread. Thus, it is important to allocate a separate budget of test-kits per day targeted towards preventing community spread and detecting new cases early on. In this report, we consider the problem of allocating test-kits and discuss some solution approaches. We believe that these approaches will be useful to contain community spread and detect new cases early on. Additionally, these approaches would help in collecting unbiased data which can then be used to improve the accuracy of machine learning models trained to predict COVID-19 infections.

COVID-19: Strategies for Allocation of Test Kits

Since its emergence in Wuhan, China in December 2019, novel Coronavirus disease - 2019 (COVID-19) has rapidly spread worldwide, achieving pandemic status on 11 (th) March, 2020. As of 1 (st) April 2020, COVID-19, which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), had infected over 800,000 people and caused over 40,000 deaths in 205 countries and territories. COVID-19 has had its heaviest toll on Europe, United States and China. As of 1 (st) of April 2020, the number of confirmed COVID-19 cases in Africa was relatively low, with the highest number registered by South Africa, which had reported 1,380 confirmed cases. On the same date (also the date of this review), Africa had reported 5,999 confirmed cases, of which 3,838 (almost 65%) occurred in South Africa, Algeria, Egypt, Morocco and Tunisia, with the remaining 2,071 cases distributed unevenly across the other African countries. We speculate that while African nations are currently experiencing much lower rates of COVID-19 relative to other continents, their significantly lower testing rates may grossly underestimate incidence rates. Failure to grasp the true picture may mean crucial windows of opportunity shut unutilized, while limited resources are not deployed to maximum effect. In the absence of extensive testing data, an overestimation of spread may lead to disproportionate measures being taken, causing avoidable strain on livelihoods and economies. Here, based on the African situation, we discuss COVID-19 diagnostic challenges and how they may blunt responses.

COVID-19: Are Africa’s diagnostic challenges blunting response effectiveness?

Covid-19: Lack of test and trace data are frustrating government scrutiny

Testing and case identification are key strategies in controlling the COVID-19 pandemic. Contact tracing and isolation are only possible if cases have been identified. The effectiveness of testing must be tracked, but a single comprehensive metric is not available to assess testing effectiveness, and no timely estimates of case detection rate are available globally, making inter-country comparisons difficult. The purpose of this paper was to propose a single, comprehensive metric, called the COVID-19 Testing Index (CovTI) scaled from 0 to 100, that incorporated several testing metrics. The index was based on case-fatality rate, test positivity rate, active cases, and an estimate of the detection rate. It used parsimonious modeling to estimate the true total number of COVID-19 cases based on deaths, testing, health system capacity, and government transparency. Publicly reported data from 188 countries and territories were included in the index. Estimates of detection rates aligned with previous estimates in literature (R2=0.97). As of June 3, 2020, the states with the highest CovTI included Iceland, Australia, New Zealand, Hong Kong, and Thailand, and some island nations. Globally, CovTI increased from April 20 ([x]=43.2) to June 3 ([x]=52.2) but declined in ca. 10% of countries. Bivariate analyses showed the average in countries with open public testing policies (59.7, 95% CI 55.6-63.8) were significantly higher than countries with no testing policy (30.2, 95% CI 18.1-42.3) (p<0.0001). A multiple linear regression model assessed the association of independent grouping variables with CovTI. Open public testing and extensive contact tracing were shown to significantly increase CovTI, after adjusting for extrinsic factors, including geographic isolation and centralized forms of government. This tool may be useful for policymakers to assess testing effectiveness, inform decisions, and identify model countries. It may also serve as a tool for researchers in analyses by combining it with other databases.

A novel comprehensive metric to assess COVID-19 testing outcomes: Effects of geography, government, and policy response

Testing representative populations to determine the prevalence or percent of the population with active SARS-Cov-2 (COVID-19) infection and/or antibodies to infection is being recommended as essential for making public policy decisions to open-up or to continue enforcing national, state and local government rules to shelter-in-place. However, all laboratory tests are imperfect and have estimates of sensitivity and specificity less than 100% - in some cases considerably less than 100%. That error will lead to biased prevalence estimates. If the true prevalence of COVID-19 is low, possibly in the range of 1-5%, then testing error will lead to a constant background of bias that will most likely be larger and possibly much larger than the true prevalence itself. As a result, what is needed is a method for adjusting prevalence estimates for testing error. In this paper we outline methods for adjusting prevalence estimates for testing error both prospectively in studies being planned and retrospectively in studies that have been conducted. The methods if employed would also help to harmonize study results within countries and around the world. Adjustment can lead to more accurate prevalence estimates and to better policy decisions.

Adjusting Coronavirus prevalence estimates for laboratory test kit error

The pandemic spread of the COVID-19 virus has, as of 20th of April 2020, reached most countries of the world. In an effort to design informed public health policies, many modelling studies have been performed to predict crucial outcomes of interest, including ICU solicitation, cumulated death counts, etc... The corresponding data analyses however, mostly rely on restricted (openly available) data sources, which typically include daily death rates and confirmed COVID cases time series. In addition, many of these predictions are derived before the peak of the outbreak has been observed yet (as is still currently the case for many countries). In this work, we show that peak phase and data paucity have a substantial impact on the reliability of model predictions. Although we focus on a recent model of the COVID pandemics, our conclusions most likely apply to most existing models, which are variants of the so-called 'Susceptible-Infected-Removed' or SIR framework. Our results highlight the need for performing systematic reliability evaluations for all models that currently inform public health policies. They also motivate a plea for gathering and opening richer and more reliable data time series (e.g., ICU occupancy, negative test rates, social distancing commitment reports, etc).

On the reliability of model-based predictions in the context of the current COVID epidemic event: impact of outbreak peak phase and data paucity

The number of new infections per day is a key quantity for effective epidemic management. It can be estimated by testing of random population samples. Without such direct epidemiological measurement, other approaches are required to infer whether the number of new cases is likely to be increasing or decreasing: for example, estimating the pathogen reproductive rate, R, using data gathered from the clinical response to the disease. For COVID-19 such R estimation is heavily dependent on modelling assumptions, because the available clinical case data are opportunistic observational data subject to severe temporal confounding. Given this difficulty it is useful to reconstruct the time course of infections from the least compromised available data, using minimal prior assumptions. A Bayesian inverse problem approach applied to UK data on COVID-19 deaths and the disease duration distribution suggests that infections were in decline before UK lockdown, and that infections in Sweden started to decline only a short time later.

Did COVID-19 infections decline before UK lockdown?

In attempting to predict the further course of the novel coronavirus disease (COVID-19) pandemic caused by SARS-CoV-2, mathematical models of different types are frequently employed and calibrated to reported case numbers. Among the major challenges in interpreting these data is the uncertainty about the amount of undetected infections, or conversely: the detection ratio. As a result, some models make assumptions about the percentage of detected cases among total infections while others completely neglect undetected cases. Here, we illustrate how model projections about case and fatality numbers vary significantly under varying assumptions on the detection ratio. Uncertainties in model predictions can be significantly reduced by representative testing, both for antibodies and active virus RNA, to uncover past and current infections that have gone undetected thus far.

The significance of case detection ratios for predictions on the outcome of an epidemic - a message from mathematical modelers

The Coronavirus Disease (Covid-19) pandemic is caused by the severe acute respiratory syndrome virus 2 and was first identified in Wuhan, China, in December 2019. The disease spread globally, leading to the World Health Organization declaring it a pandemic in March 2020. The condition is often fatal in its severe form. As it is a previously unknown virus, no treatment is identified or any vaccine available. The burden of disease control and containment, therefore, falls upon a robust and geographically appropriate testing strategy. Testing policies are modified, in turn, by the rapidly evolving patterns of the disease in various nations and by the evolving nature of tests in development. It is, therefore, helpful to study different national models to learn from the experience of different countries. This article compares testing strategies in the UK and India as the two countries travel different paths in controlling the pandemic. The UK is one of the most severely affected countries in the world. Initially restricted to hospitalised patients, the UK has broadened the scope of testing to many categories of individuals. In contrast, India appears to have a lower prevalence of the infection. However, the large Indian population and relatively insufficient testing capacities so far have led India to adopt a different testing trajectory, with the testing currently focused on high-risk groups in the community and hospitals. Owing to the rapidly changing nature of the disease, there can be no ‘one-size-fits-all’ policy but should be based on country-specific circumstances.

Testing times in Coronavirus disease (Covid-19): A tale of two nations

Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is spreading at a rapid pace throughout the world, and the World Health Organization (WHO) declared it as pandemic on March 11, 2020. We present a case of COVID-19 patient whose reverse transcription-polymerase chain reaction (RT-PCR) initially was false negative and later turned positive, which will stress the importance of a comprehensive approach while evaluating a patient with a differential of COVID-19. The clinicians should be aware of the sensitivity and specificities of these tests which can have grave implications on the patient and community if the diagnosis is missed just based on the laboratory tests due to the highly contagious nature of the disease.

A Comprehensive Approach Is Vital for Diagnosing COVID-19: A Case of False Negative

We propose a novel testing and containment strategy in order to contain the spread of SARS-CoV2 while permitting large parts of the population to resume social and economic activity. Our approach recognises the fact that testing capacities are severely constrained in many countries. In this setting, we show that finding the best way to utilise this limited number of tests during a pandemic can be formulated concisely as an allocation problem. Our problem formulation takes into account the heterogeneity of the population and uses pooled testing to identify and isolate individuals while prioritising key workers and individuals with a higher risk of spreading the disease. In order to demonstrate the efficacy of our testing and containment mechanism, we perform simulations using a network-based SIR model. Our simulations indicate that applying our mechanism on a population of $100,000$ individuals with only $16$ tests per day reduces the peak number of infected individuals by approximately $20\%$, when compared to the scenario where no intervention is implemented.

Maximising the Benefits of an Acutely Limited Number of COVID-19 Tests

The recent emergence of the COVID-19 pandemic has posed an unprecedented healthcare challenge and catastrophic economic and social consequences to the countries across the world. The situation is even worse for emerging economies like India. WHO recommends mass scale testing as one of the most effective ways to contain its spread and fight the pandemic. But, due to the high cost and shortage of test kits, specifically in India, the testing is restricted to only those who are symptomatic. In this context, pooled testing is recommended by some experts as a partial solution to overcome this problem. In this article, we explain the basic statistical theory behind the pooled testing procedure for screening as well as prevalence estimation. In real world situations, the tests are imperfect, and lead to false positive and false negative results. We provide theoretical explanation of the impact of these diagnostic errors on the performances of individual testing and pooled testing procedures. Finally, we study the effect of misspecification of sensitivity and specificity of tests on the estimate of prevalence, an issue, which is debated a lot among the scientists in the context of COVID-19. Our theoretical investigations lead to some interesting and precise understanding of some of these issues.

Application of pooled testing in screening and estimating the prevalence of Covid-19

The COVID-19 pandemic has plagued the world for months. The U.S. has taken measures to counter it. On a daily basis, newly confirmed cases have been reported. In the early days, these numbers showed an increasing trend. Recently, the numbers have been generally flattened out. This report tries to estimate the hidden number of currently alive infections in the population by using the confirmed cases. A major result indicates an existing infections estimate at about 10-50 times the daily confirmed new cases, with the stringent social distancing policy tipping to the upper end of this range. It clarifies the relationship between the infection rate and the test rate to put the epidemic under control, which says that the test rate shall keep up at the same pace as infection rate to prevent an outbreak. This relationship is meaningful in the wake of business re-opening in the U.S. and the world. The report also reveals the connections of all the measures taken to the epidemic spread. A stratified sampling method is proposed to add to the current tool kits of epidemic control. Again, this report is a summary of some straight observations and thoughts, not through a thorough study backed with field data. The results appear obvious and suitable for general education to interested policymakers and the public.

Controlling the Hidden Growth of COVID-19

I estimate plausible true positive (TP) rates for the number of COVID-19 tests per day, most relevant when the number of test is on the same order of magnitude as number of infected persons. I then modify a standard SEIR model to model current growth patterns and detection rates in South Korea and New York state. Although reducing transmission rates have the largest impact, increasing TP rates by ~10% in New York can have an impact equal to adding tens of thousands of new tests per day. Increasing both TP rates and tests per day together can have significant impacts and likely be more easily sustained than social distancing restrictions. Systematic and standardized data collection, even beyond contact tracking, should be ongoing and quickly made available for research teams to maximize the efficacy of testing.

Predicting Whom to Test is More Important Than More Tests - Modeling the Impact of Testing on the Spread of COVID-19 Virus By True Positive Rate Estimation

Detection of SARS-CoV-2 infections to date has relied heavily on RT-PCR testing. However, limited test availability, high false-negative rates, and the existence of asymptomatic or sub-clinical infections have resulted in an under-counting of the true prevalence of SARS-CoV-2. Here, we show how influenza-like illness (ILI) outpatient surveillance data can be used to estimate the prevalence of SARS-CoV-2. We found a surge of non-influenza ILI above the seasonal average in March 2020 and showed that this surge correlated with COVID-19 case counts across states. If 1/3 of patients infected with SARS-CoV-2 in the US sought care, this ILI surge would have corresponded to more than 8.7 million new SARS-CoV-2 infections across the US during the three-week period from March 8 to March 28, 2020. Combining excess ILI counts with the date of onset of community transmission in the US, we also show that the early epidemic in the US was unlikely to have been doubling slower than every 4 days. Together these results suggest a conceptual model for the COVID-19 epidemic in the US characterized by rapid spread across the US with over 80% infected patients remaining undetected. We emphasize the importance of testing these findings with seroprevalence data and discuss the broader potential to use syndromic surveillance for early detection and understanding of emerging infectious diseases.

Using influenza surveillance networks to estimate state-specific prevalence of SARS-CoV-2 in the United States

The COVID-19 pandemic (SARS-CoV-2 virus) is the defying global health crisis of our time. The absence of mass testing and the relevant presence of asymptomatic individuals causes the available data of the COVID-19 pandemic in Brazil to be largely under-reported regarding the number of infected individuals and deaths. We propose an adapted Susceptible-Infected-Recovered (SIR) model which explicitly incorporates the under-reporting and the response of the population to public policies (such as confinement measures, widespread use of masks, etc) to cast short-term and long-term predictions. Large amounts of uncertainty could provide misleading models and predictions. In this paper, we discuss the role of uncertainty in these prediction, which is illustrated regarding three key aspects. First, assuming that the number of infected individuals is under-reported, we demonstrate an anticipation regarding the peak of infection. Furthermore, while a model with a single class of infected individuals yields forecasts with increased peaks, a model that considers both symptomatic and asymptomatic infected individuals suggests a decrease of the peak of symptomatic. Second, considering that the actual amount of deaths is larger than what is being register, then demonstrate the increase of the mortality rates. Third, when consider generally under-reported data, we demonstrate how the transmission and recovery rate model parameters change qualitatively and quantitatively. We also investigate the effect of the"COVID-19 under-reporting tripod", i.e. the under-reporting in terms of infected individuals, of deaths and the true mortality rate. If two of these factors are known, the remainder can be inferred, as long as proportions are kept constant. The proposed approach allows one to determine the margins of uncertainty by assessments on the observed and true mortality rates.

The COVID-19 (SARS-CoV-2) Uncertainty Tripod in Brazil: Assessments on model-based predictions with large under-reporting

Background In response to the SARS-CoV2 pandemic, governments have adopted a variety of public health measures. There are variations in how much testing has been done across countries. South Korea, Germany, and Iceland take the bet of massive testing of their population. Whereas tests were not performed widely in southern European countries. As the former undergo a lower case-fatality rate due to the COVID-19 than the latter, the impact of the testing strategy must be investigated. In this study, we aimed to evaluate the impact of testing on the case fatality rate. Methods We use data on inpatients across French geographic areas and propose a novel methodology that exploits policy discontinuities at region borders to estimate the effect of COVID-19 tests on the case-fatality rate. In France, testing policies are determined locally. We compare all contiguous department pairs located on the opposite sides of a region border. The heterogeneity in testing rate between department pairs together with the similarities in other dimensions allow us to mimic the existence of treatment and control groups and to identify the impact of testing on mortality. Results The increase of one percentage point in the test rate is associated with a decrease of 0.001 percentage point in the death rate. In other words, for each additional 1000 tests, one person would have remained alive. Conclusion Massive population testing could have a significant effect on mortality in different ways. Mass testing may help decision-makers to implement healthcare measures to limit the spread of the disease.

Impact of virus testing on COVID-19 case fatality rate: estimate using a fixed-effects model

Confirmed cases in Australia notified up to 17 May 2020: notifications = 7,075; deaths = 100. The incidence of new cases of COVID-19 has reduced dramatically since a peak in mid-March. Social distancing measures, public health action and the reduction in international travel have likely been effective in slowing the spread of the disease, in the Australian community. Testing rates over the past week have increased markedly, with a continued very low proportion of people testing positive. These low rates of detection are indicative of low levels of COVID-19 transmission. It is important that testing rates and community adherence to public health measures remain high to support the continued suppression of the virus, particularly in vulnerable high-risk groups and settings. New cases of COVID-19 are currently being reported by by only some jurisdictions, albeit at relatively low rates. Although case numbers are low, new cases tend to still be a mix of overseas-acquired and locally-acquired infections. Most locally-acquired cases can be linked back to a known case or cluster. Although the proportion of locally-acquired cases has increased, the overall rate of new cases, regardless of place of acquisition, continues to decrease. The crude case fatality rate in Australia remains low (1.4%), compared with the WHO reported global rate (6.9%). The low case fatality rate is likely reflective of high case detection and high quality of health care services in Australia. Deaths from COVID-19 in Australia have occurred predominantly among the elderly and those with comorbidities, with no deaths occurring in those under 40 years. The highest rate of COVID-19 continues to be among people aged 60-79 years. One third of all cases in this age group have been associated with several outbreaks linked to cruise ships. The lowest rate of disease is in young children, a pattern reflected in international reports. Internationally, while the number of new cases each day remains relatively stable at the global level, some areas such as Brazil and India are showing a dramatic rise in reported cases. Although some low-income countries have so far reported few cases, it is possible that this is due to limited diagnostic and public health capacity, and may not be reflective of true disease incidence.

COVID-19, Australia: Epidemiology Report 16 (Reporting week to 23:59 AEST 17 May 2020).

Studies aimed at characterizing the evolution of COVID-19 disease often rely on case-based surveillance data publicly released by health authorities, that can be incomplete and prone to errors. Here, we quantify the biases caused by the use of inaccurate data in the estimation of the Time-Varying Reproduction Number R(t). By focusing on Italy and Spain, two of the hardest-hit countries in Europe and worldwide, we show that if the symptoms' onset time-series is inferred from the notification date series, the R(t) curve cannot capture nor describe accurately the early dynamics of the epidemic. Furthermore, the effectiveness of the containment measures that were implemented, such as national lockdowns, can be properly evaluated only when R(t) is estimated using the real time-series of dates of symptoms' onset. Our findings show that extreme care should be taken when a pivotal quantity like R(t) is used to make decisions and to evaluate different alternatives.

Impact of the accuracy of case-based surveillance data on the estimation of time-varying reproduction numbers

As the COVID-19 pandemic spreads across the world, it is important to understand its features and responses to public health interventions in real-time. The field of infectious diseases epidemiology has highly advanced modeling strategies that yield relevant estimates. These include the doubling time of the epidemic and various other representations of the numbers of cases identified over time. Crude estimates of these quantities suffer from dependence on the underlying testing strategies within communities. We clarify the functional relationship between testing and the epidemic parameters, and thereby derive sensitivity analyses that explore the range of possible truths under various testing dynamics. We derive the required adjustment to the estimates of interest for New York City. We demonstrate that crude estimates that assume stable testing or complete testing can be biased.

Accounting for incomplete testing in the estimation of epidemic parameters

COVID-19, Australia: Epidemiology Report 16 (Reporting week to 23:59 AEST 17 May 2020)

We paired high-resolution travel-time metrics with a SARS-CoV-2 testing location database in the United States. Median travel time to testing sites is longer in counties with lower population density, and a higher percent of minority and uninsured individuals. Differential geographic accessibility to testing can recapitulate healthcare disparities and bias transmission estimates.

Geographic access to United States SARS-CoV-2 testing sites highlights healthcare disparities and may bias transmission estimates

Abstract Background With its epicenter in Wuhan, China, the COVID-19 outbreak was declared a pandemic by the World Health Organization (WHO). While many countries have implemented flight restrictions to China, an increasing number of cases with or without travel background to China are confirmed daily. These developments support concerns on possible unidentified and unreported international COVID-19 cases, which could lead to new local disease epicenters. Methods We have analyzed all available data on the development of international COVID-19 cases from January 20th, 2020 until February 18th, 2020. COVID-19 cases with and without travel history to China were divided into cohorts according to the Healthcare Access and Quality Index (HAQ-Index) of each country. Chi-square and Post-hoc testing were performed. Results While COVID-19 cases with travel history to China seem to peak for each HAQ-cohort, the number of non-travel related COVID-19 cases seem to continuously increase in the HAQ-cohort of countries with higher medical standards. Further analyses demonstrate a significantly lower proportion of reported COVID-19 cases without travel history to China in countries with lower HAQ (HAQ I vs. HAQ II, posthoc p < 0.01). Conclusions Our data indicate that countries with lower HAQ-index may either underreport COVID-19 cases or are unable to adequately detect them. Although our data may be incomplete and must be interpreted with caution, inconsistencies in reporting COVID-19 cases is a serious problem which might sabotage efforts to contain the virus.

Internationally lost COVID-19 cases

The publicly available data on COVID-19 cases provides an opportunity to better understand this new disease. However, strong attention needs to be paid to the limitations of the data to avoid making inaccurate conclusions. This article, which focuses on the relationship between the weather and COVID-19, raises the concern that the same factors influencing the spread of the disease might also affect the number of tests performed and who gets tested. For example, weather conditions impact the prevalence of respiratory diseases with symptoms similar to COVID-19, and this will likely influence the number of tests performed. This general limitation could severely undermine any similar analysis using existing COVID-19 data or similar epidemiological data, which could, therefore, mislead decision-makers on questions of great policy relevance.

The Challenge of Using Epidemiological Case Count Data: The Example of Confirmed COVID-19 Cases and the Weather

Since the beginning of March 2020, the cumulative numbers of cases of infection with the novel coronavirus SARS-CoV-2 in Germany have been reported on a daily basis. The reports originate from national laws, according to which positive test findings must be submitted to the Federal Health Authorities, the Robert Koch Institute, via the local health authorities. Since an enormous number of unreported cases can be expected, the question of how widespread the disease has been in the population cannot be answered based on these administrative reports. Using mathematical modeling, however, estimates can be made. These estimates indicate that the small numbers of diagnostic tests carried out at the beginning of the outbreak overlooked considerable parts of the infection. In order to cover the initial phase of future waves of the disease, wide-spread and comprehensive tests are recommended.

Estimation of the actual disease occurrence based on official case numbers during a COVID outbreak in Germany 2020

Background: In December 2019, an outbreak of respiratory illness caused by a novel coronavirus (2019-nCoV) emerged in Wuhan, China and has swiftly spread to other parts of China and a number of foreign countries. The 2019-nCoV cases might have been under-reported roughly from 1 to 15 January 2020, and thus we estimated the number of unreported cases and the basic reproduction number, R(0), of 2019-nCoV. Methods: We modelled the epidemic curve of 2019-nCoV cases, in mainland China from 1 December 2019 to 24 January 2020 through the exponential growth. The number of unreported cases was determined by the maximum likelihood estimation. We used the serial intervals (SI) of infection caused by two other well-known coronaviruses (CoV), Severe Acute Respiratory Syndrome (SARS) and Middle East Respiratory Syndrome (MERS) CoVs, as approximations of the unknown SI for 2019-nCoV to estimate R(0). Results: We confirmed that the initial growth phase followed an exponential growth pattern. The under-reporting was likely to have resulted in 469 (95% CI: 403–540) unreported cases from 1 to 15 January 2020. The reporting rate after 17 January 2020 was likely to have increased 21-fold (95% CI: 18–25) in comparison to the situation from 1 to 17 January 2020 on average. We estimated the R(0) of 2019-nCoV at 2.56 (95% CI: 2.49–2.63). Conclusion: The under-reporting was likely to have occurred during the first half of January 2020 and should be considered in future investigation.

Estimating the Unreported Number of Novel Coronavirus (2019-nCoV) Cases in China in the First Half of January 2020: A Data-Driven Modelling Analysis of the Early Outbreak

Covid-19: Lack of testing in Brazil is a "major failure," says MSF.

Background: COVID-19 originated in China and has quickly spread worldwide causing a pandemic. Countries need rapid data on the prevalence of the virus in communities to enable rapid containment. However, the equipment, human and laboratory resources required for conducting individual RT-PCR is prohibitive. One technique to reduce the number of tests required is the pooling of samples for analysis by RT-PCR prior to testing. Methods: We conducted a mathematical analysis of pooling strategies for infection rate classification using group testing and for the identification of individuals by testing pooled clusters of samples. Findings: On the basis of the proposed pooled testing strategy we calculate the probability of false alarm, the probability of detection, and the average number of tests required as a function of the pool size. We find that when the sample size is 256, with a maximum pool size of 64, with only 7.3 tests on the average, we can distinguish between prevalences of 1% and 5% with a probability of detection of 95% and probability of false alarm of 4%. Interpretation: The pooling of RT-PCR samples is a cost-effective technique for providing much-needed course-grained data on the prevalence of COVID-19. This is a powerful tool in providing countries with information that can facilitate a response to the pandemic that is evidence-based and saves the most lives possible with the resources available.

Pooling RT-PCR or NGS samples has the potential to cost-effectively generate estimates of COVID-19 prevalence in resource limited environments

Speed is critical in the response to COVID-19 So why has the United States been so slow in its attempt to develop reliable diagnostic tests and use them widely? The World Health Organization (WHO) has shipped testing kits to 57 countries China had five commercial tests on the market 1 month ago and can now do up to 1 6 million tests a week;South Korea has tested 65,000 people so far The U S Centers for Disease Control and Prevention (CDC), in contrast, has done only 459 tests since the epidemic began The rollout of a CDC-designed test kit to state and local labs has become a fiasco because it contained a faulty reagent Labs around the country eager to test more suspected cases—and test them faster—have been unable to do so No commercial or state labs have the approval to use their own tests In what is already an infamous snafu, CDC initially refused a request to test a patient in Northern California who turned out to be the first probable COVID19 case without known links to an infected person

The United States badly bungled coronavirus testing—but things may soon improve ;Science ;AAAS

Background: Cases with negative reverse transcription-polymerase chain reaction (RT-PCR) results at initial testing for suspicion of SARS-CoV-2 infection, and found to be positive in a subsequent test, are considered as RT-PCR false-negative cases. False-negative cases have important implications for COVID-19 management, isolation, and risk of transmission. We aimed to review and critically appraise evidence about the proportion of RT-PCR false-negatives at initial testing for COVID-19. Methods: We performed a systematic review and critical appraisal of literature with high involvement of stakeholders in the review process. We searched on MEDLINE, EMBASE, LILACS, the WHO database of COVID-19 publications, the EPPI-Centre living systematic map of evidence about COVID-19, and the living systematic review developed by the University of Bern (ISPM). Two authors screened and selected studies according to the eligibility criteria and collected data of included studies (no-independent verification). Risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. We calculated the false-negative proportion with the corresponding 95% CI using a multilevel mixed-effect logistic regression model using STATA 16. Certainty of the evidence about false-negative cases was rated using the GRADE approach for tests and strategies. The information is current up to 6 April 2020. Findings: Five studies enrolling 957 patients were included. All studies were affected by several biases and applicability concerns. Pooled estimation of false-negative proportion was 0.085 (95% CI= 0.034 to 0.196; tau-squared = 1.08; 95% CI= 0.27 to 8.28; p<0.001); however, this estimation is highly affected by unexplained heterogeneity, and its interpretation should be avoided. The certainty of the evidence was judged as very low, due to the risk of bias, indirectness, and inconsistency issues. Conclusions: The collected evidence has several limitations, including risk of bias issues, high heterogeneity, and concerns about its applicability. Nonetheless, our findings reinforce the need for repeated testing in patients with suspicion of SARS-Cov-2 infection given that up to 29% of patients could have an initial RT-PCR false-negative result. Systematic review registration: Protocol available on OSF website: https://osf.io/gp38w/

FALSE-NEGATIVE RESULTS OF INITIAL RT-PCR ASSAYS FOR COVID-19: A SYSTEMATIC REVIEW

Background: A novel coronavirus disease 2019 (COVID-19) broke out in Wuhan of Hubei province and had spread throughout the world since December 2019. Because the clinically diagnosed cases in Hubei province were reported for the first time on February 13, 2020, a very high peak of new cases in China was observed. The reason why so many clinically diagnosed cases appeared was not clear. Methods: All data of new cases in China were acquired from WHO situation reports. Linear fitting was used to infer the ability to detect COVID-19 infections. Primer-BLAST and nucleotide blast were applied to check the specificity of primers. Expression data of human mRNA in different tissues was obtained from Human Protein Atlas. Finding: Based on the data and analysis of changes of new laboratory-confirmed cases and new clinically diagnosed cases, it was inferred that there were many false-negative results in all clinically diagnosed cases in Hubei province. There were eight non-specific primers in dozens of primers used in clinical or research detection of COVID-19. Among them, a pair of primer for the ORF1ab regions of SARS-CoV-2 genome, which widely applied to detect SARS-CoV-2 virus in China, well matched some human mRNAs such as Cathepsin C transcripts. Compared to other transcripts, Cathepsin C mRNA had a high abundance in tonsil, lung and small intestine. Interpretation: Some non-specific RT-PCR primers could cause the serious interference during RT-PCR amplification so as to increase the risk of false-negative diagnoses for COVID-19 infections. Funding Key Research Project of the Higher Education of Henan Province

Non-specific Primers Reveal False-negative Risk in Detection of COVID-19 Infections

There is some consensus in Europe and Asia about testing rates being crucial to controlling COVID-19 pandemics. There are though misconceptions on what means an effective high testing rate. This paper demonstrates that the rate of tests per detected case (Tests/Case) is the important variable, correlating negatively with the number of deaths. The higher the Tests/Case rate, the lower the death rate, as this predictor is causally related to contact tracing and isolation of the vectors of the disease. Doubling Tests/Case typically divides by three the number of deaths. On the other hand, per capita testing rate is a poor predictor for the performance of policies to fight the pandemics. The number of tests per 1,000 inhabitants (Tests/1,000) tends to correlate positively with the number of deaths. In some cases, high levels of Tests/1,000 just mean an epidemic that ran out of control, with an explosion of cases that demands high testing rates just to confirm the diagnosis of the very sick.

Testing for tracing or testing just for treating? A comparative analysis between strategies to face COVID-19 pandemic.

COVID-19 testing studies have become a standard approach for estimating prevalence and fatality rates which then assist in public health decision making to contain and mitigate the spread of the disease. The sampling designs used are often biased in that they do not reflect the true underlying populations. For instance, individuals with strong symptoms are more likely to be tested than those with no symptoms. This results in biased estimates of prevalence (too high) and over-estimation of fatality rates. Typical post-sampling corrections are not always possible. Here we present a simple bias correction methodology derived and adapted from a correction for publication bias in meta analysis studies. The methodology is general enough to allow a wide variety of customization making it more useful in practice. Implementation is easily done using already collected information. We show via an example that the bias corrections can provide dramatic reductions in estimation error.

Estimation of testing bias in covid-19

As of January 22, 2020, "disease caused by a novel coronavirus" became a reportable disease of public health significance in Ontario. Public health units were provided with guidance on the entry of patients tested for severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), the virus causing 2019 coronavirus disease (COVID-19), into the provincial public health information system. Between January 22 and February 22, 2020, there were 359 individuals who had a negative test result recorded and three confirmed cases of COVID-19. Of those who tested negative, 51% were female and 71% were under 50 years of age. The most common symptoms reported were cough (55%), fever (37%) and sore throat (35%). The majority were tested within three days of symptom onset, but over one-quarter tested more than seven days after symptom onset. Over the first month of reportability, reported travel history shifted from China to an increasing proportion with travel outside of China.

Surveillance of persons-who tested negative for COVID-19 in Ontario, January 22-February 22, 2020.

The world is suffering from a pandemic called COVID-19, caused by the SARS-CoV-2 virus. National governments have problems evaluating the reach of the epidemic, due to having limited resources and tests at their disposal. This problem is especially acute in low and middle-income countries (LMICs). Hence, any simple, cheap and flexible means of evaluating the incidence and evolution of the epidemic in a given country with a reasonable level of accuracy is useful. In this paper, we propose a technique based on (anonymous) surveys in which participants report on the health status of their contacts. This indirect reporting technique, known in the literature as network scale-up method, preserves the privacy of the participants and their contacts, and collects information from a larger fraction of the population (as compared to individual surveys). This technique has been deployed in the CoronaSurveys project, which has been collecting reports for the COVID-19 pandemic for more than two months. Results obtained by CoronaSurveys show the power and flexibility of the approach, suggesting that it could be an inexpensive and powerful tool for LMICs.

CoronaSurveys: Using Surveys with Indirect Reporting to Estimate the Incidence and Evolution of Epidemics

Reverse transcription-polymerase chain reaction (RT-PCR) assays are used to test patients and key workers for infection with the causative SARS-CoV-2 virus. RT-PCR tests are highly specific and the probability of false positives is low, but false negatives can occur if the sample contains insufficient quantities of the virus to be successfully amplified and detected. The amount of virus in a swab is likely to vary between patients, sample location (nasal, throat or sputum) and through time as infection progresses. Here, we analyse publicly available data from patients who received multiple RT-PCR tests and were identified as SARS-CoV-2 positive at least once. We identify that the probability of a positive test decreases with time after symptom onset, with throat samples less likely to yield a positive result relative to nasal samples. Empirically derived distributions of the time between symptom onset and hospitalisation allowed us to comment on the likely false negative rates in cohorts of patients who present for testing at different clinical stages. We further estimate the expected numbers of false negative tests in a group of tested individuals and show how this is affected by the timing of the tests. Finally, we assessed the robustness of these estimates of false negative rates to the probability of false positive tests. This work has implications both for the identification of infected patients and for the discharge of convalescing patients who are potentially still infectious.

Estimating false-negative detection rate of SARS-CoV-2 by RT-PCR

The reported number of new cases underestimates the real spread of COVID-19 pandemic because of non-tested asymptomatic people and limited global access to reliable diagnostic tests. In this context, COVID-19 mortality with confirmed diagnosis becomes an attractive source of information to be included in the analysis of perspectives and proposals. Objective data are required to calculate the capacity of resources provided by health systems. New strategies are needed to stabilize or minimize the mortality surge. However, we will not afford this goal until more alternatives were available. We still need an effective treatment, an affordable vaccine, or a collective achievement of sufficient immunity (reaching up to 70% of the whole population). At any time, the arriving waves of the pandemic are testing the capacity of governments. The health services struggle to keep the plateau in a steady-state below 100 deaths per million inhabitants. Therefore, it is necessary to increase the alternatives and supplies based on the current and near-future expected demands imposed by the number of deaths by COVID-19. Estimating COVID-19 mortality in various scenarios with the gradual release of social constraints will help predict the magnitude of those arriving waves.

COVID-19 Waves: Importance of Accumulative Mortality per Million Inhabitants.

In December-2019 China reported several cases of a novel coronavirus later called COVID-19. In this work, we will use a probabilistic method for approximating the true daily numbers of infected. Based on two distribution functions to describe the spontaneous recovered cases on the one hand and the detected cases on the other hand. The impact of the underlying variables of these functions is discussed. The detected rate is predicted to be between 5.3% and 10,8%, which means that there would be about 38 million infected until now (10-May 2020), rather than the officially declared number of 3.99 million worldwide cases.

On the true numbers of COVID-19 infections: behind the available data

Different COVID-19 testing approaches have been implemented among Italian regions, reflected in heterogeneous testing rates. We analysed the number COVID-19-related deaths in relation to the number of tests performed among the most hit Italian regions. We showed that regions with the highest number of tests performed (Veneto and Toscana) had the lowest 30-day crude mortality rate per 100,000 inhabitants. In addition, an inverse association between crude mortality rates and tests performed (mortality rate ratio for unit increase in tests per 1,000 inhabitants: 0.92; 95% CI 0.89-0.94) was observed. Early identification and isolation of active cases (including asymptomatic or mildly symptomatic subjects) could have had an important effect in lowering COVID-19 mortality.

Higher testing coverage is associated with lower COVID-19 mortality rate: insights from Italian regions.

Surveillance of persons-who tested negative for COVID-19 in Ontario, January 22-February 22, 2020

OBJECTIVES: To determine the public health surveillance severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) testing volume needed, both for acute infection and seroprevalence. METHODS: Required testing volumes were developed using standard statistical methods based on test analytical performance, disease prevalence, desired precision, and population size. RESULTS: Widespread testing for individual health management cannot address surveillance needs. The number of people who must be sampled for public health surveillance and decision making, although not trivial, is potentially in the thousands for any given population or subpopulation, not millions. CONCLUSIONS: While the contributions of diagnostic testing for SARS-CoV-2 have received considerable attention, concerns abound regarding the availability of sufficient testing capacity to meet demand. Different testing goals require different numbers of tests and different testing strategies; testing strategies for national or local disease surveillance, including monitoring of prevalence, receive less attention. Our clinical laboratory and diagnostic infrastructure are capable of incorporating required volumes for many local, regional, and national public health surveillance studies into their current and projected testing capacity. However, testing for surveillance requires careful design and randomization to provide meaningful insights.

Linking Statistics With Testing Policy to Manage COVID-19 in the Community

Confirmed Case Data have been widely cited during the current COVID-19 pandemic as an estimate of the spread of the SARS-CoV-2 virus. However, their central role in media, official reports and decision-making may be undeserved and misleading. Previously published Infection Fatality Rates were weighted by age structure in the 50 countries with more reported deaths to obtain country-specific rates. For each country, the number of infections up to the Infection Date (23 days ago = Incubation Period + Onset to Death period) and the present percentage of immune population were estimated using Infection Fatality Rate, the number of reported deaths (which is less prone to undersampling), and projecting back to Infection Date. We then estimated a Detection Index for each country as the percentage of estimated infections that confirmed cases represent. Assuming that detection remains constant after Infection Date, we estimated the number of deaths and the estimated percentage of the population of each country expected to be immune up to 23 days into the future. Estimated Infection Fatality Rates are higher in Europe. In most countries, confirmed cases currently represent less than 30% of estimated infections on Infection Date, and this value decreases with time. Countries with flat curves throughout the pandemic show the lowest immunity percentages and these values seem unlikely to change in the near future, suggesting that they remain vulnerable to new outbreaks. Estimates for some countries with low Infection Fatality Rates suggest a still steep increase in the number of casualties in the next three weeks. Countries that did not control initial outbreaks seem to have reached higher immunity percentages, although mostly still under 5%. We provide the code to monitor the trajectories of these estimates in 178 countries throughout the COVID-19 pandemic.

The misleading illusion of COVID-19 confirmed case data: alternative estimates and a monitoring tool

We estimate the number of COVID-19 cases from newly reported deaths in a population without previous reports. Our results suggest that by the time a single death occurs, hundreds to thousands of cases are likely to be present in that population. This suggests containment via contact tracing will be challenging at this point, and other response strategies should be considered. Our approach is implemented in a publicly available, user-friendly, online tool.

Inferring the number of COVID-19 cases from recently reported deaths

Policymakers need a clear and fast assessment of the real spread of the epidemic of COVID-19 in each of their respective countries. Standard measures of the situation provided by the governments include reported positive cases and total deaths. While total deaths immediately indicate that countries like Italy and Spain have the worst situation as of mid April 2020, on its own, reported cases do not provide a correct picture of the situation. The reason is that different countries diagnose diversely and present very distinctive reported case fatality rate (CFR). The same levels of reported incidence and mortality might hide a very different underlying picture. Here we present a straightforward and robust estimation of the diagnostic rate in each European country. From that estimation we obtain an uniform unbiased incidence of the epidemic. The method to obtain the diagnostic rate is transparent and empiric. The key assumption of the method is that the real CFR in Europe of COVID-19 is not strongly country-dependent. We show that this number is not expected to be biased due to demography nor the way total deaths are reported. The estimation protocol has a dynamic nature, and it has been giving converging numbers for diagnostic rates in all European countries as of mid April 2020. From this diagnostic rate, policy makers can obtain an Effective Potential Growth (EPG) updated everyday providing an unbiased assessment of the countries with more potential to have an uncontrolled situation. The method developed will be used to track possible improvements on the diagnostic rate in European countries as the epidemic evolves.

Robust estimation of diagnostic rate and real incidence of COVID-19 for European policymakers

To optimize epidemiologic interventions, predictors of mortality should be identified. The US COVID-19 epidemic data, reported up to 31 March 2020, were analyzed using kernel regularized least squares regression. Six potential predictors of mortality were investigated: (i) the number of diagnostic tests performed in testing week I; (ii) the proportion of all tests conducted during week I of testing; (iii) the cumulative number of (test-positive) cases through 3-31-2020, (iv) the number of tests performed/million citizens; (v) the cumulative number of citizens tested; and (vi) the apparent prevalence rate, defined as the number of cases/million citizens. Two metrics estimated mortality: the number of deaths and the number of deaths/million citizens. While both expressions of mortality were predicted by the case count and the apparent prevalence rate, the number of deaths/million citizens was {approx}3.5 times better predicted by the apparent prevalence rate than the number of cases. In eighteen states, early testing/million citizens/population density was inversely associated with the cumulative mortality reported by 31 March, 2020. Findings support the hypothesis that early and massive testing saves lives. Other factors --e.g., population density-- may also influence outcomes. To optimize national and local policies, the creation and dissemination of high resolution geo-referenced, epidemic data is recommended.

Early and massive testing saves lives: COVID-19 related infections and deaths in the United States during March of 2020

Covid-19: Lack of capacity led to halting of community testing in March, admits deputy chief medical officer.

Confirmed cases in Australia notified up to 24 May 2020: notifications = 7,135; deaths = 102. The incidence of COVID-19 has markedly reduced since a peak in mid-March. There have been no cases reported in SA, the NT or the ACT in the last four weeks. The numbers of new cases reported from other jurisdictions continue to be very low. Testing rates have been higher across all jurisdictions, with Victoria reporting an 85% testing rate increase and NSW a 40% increase over this period. The positivity rate nationally continues to remain very low at less than 0.1% over the reporting period. Continued high rates of testing are necessary to detect and mitigate the spread of COVID-19 in the community. Over the past fortnight, 45% of cases acquired their infection overseas. Of cases considered to be locally acquired over this period, most were associated with contacts of confirmed cases or were associated with known outbreaks. The highest rate of COVID-19 continues to be among people aged 65-79 years. Three-quarters of all cases in this age group have been associated with overseas travel, including several outbreaks linked to cruise ships. The lowest rate of disease is in children under 18, a pattern reflected in international reports. A small proportion of cases overall have experienced severe disease, requiring hospitalisation or intensive care with some fatalities. The crude case fatality rate amongst Australian cases is 1.4%. People who are older and have one or more comorbidities are more likely to experience severe disease. A combination of early case identification, physical distancing, public health measures and a reduction in international travel have likely been effective in slowing the spread of the disease in Australia. In addition, the median number of days between symptom onset and diagnostic testing has improved considerably from 7 days in the early phase of the outbreak to 1 day in the latest phase of the epidemic. Internationally, as at 24 May 2020, there have been recent increases in the number of daily cases reported globally. The largest numbers of both cases and deaths have been reported in the United States. Of the confirmed cases reported globally, the case fatality rate is approximately 6.5%. Countries in South America are starting to see rapid acceleration, while the United States is seeing a very slow decline in its daily new case numbers. In the South East Asia region, India and Bangladesh are seeing accelerating epidemics, compounded by the recovery from Cyclone Amphan. Increasing numbers of cases are also being reported in Africa, although the numbers are much smaller. In the Pacific there are very few daily new cases reported.

COVID-19, Australia: Epidemiology Report 17 (Fortnightly reporting period ending 24 May 2020)

This article offers a reflection on the testing strategies deployed in the generation of epidemiological data in the European Union (EU). I will argue that, while in the early days of the pandemic, Member States proceeded to testing in a rather scattered way, the shortage of resources seems to have acted as a driver of coordination, which is now increasingly being discussed at EU level. I will examine the legal and institutional framework supporting such embryonic coordination efforts and offer a preliminary assessment of their implications for a European approach to epidemiological knowledge-making.

I Just Can’t Get Enough (of Experts): The Numbers of COVID-19 and the Need for a European Approach to Testing

The relative case fatality rates (CFRs) between groups and countries are key measures of relative risk that guide policy decisions regarding scarce medical resource allocation during the ongoing COVID-19 pandemic. In the middle of an active outbreak when surveillance data is the primary source of information, estimating these quantities involves compensating for competing biases in time series of deaths, cases, and recoveries. These include time- and severity- dependent reporting of cases as well as time lags in observed patient outcomes. In the context of COVID-19 CFR estimation, we survey such biases and their potential significance. Further, we analyze theoretically the effect of certain biases, like preferential reporting of fatal cases, on naive estimators of CFR. We provide a partially corrected estimator of these naive estimates that accounts for time lag and imperfect reporting of deaths and recoveries. We show that collection of randomized data by testing the contacts of infectious individuals regardless of the presence of symptoms would mitigate bias by limiting the covariance between diagnosis and death. Our analysis is supplemented by theoretical and numerical results and a simple and fast open-source codebase at https://github.com/aangelopoulos/cfr-covid-19 .

On Identifying and Mitigating Bias in the Estimation of the COVID-19 Case Fatality Rate

The number of confirmed cases of COVID-19 is often used as a proxy for the actual number of ground truth COVID-19 infected cases in both public discourse and policy making. However, the number of confirmed cases depends on the testing policy, and it is important to understand how the number of positive cases obtained using different testing policies reveals the unknown ground truth. We develop an agent-based simulation framework in Python that can simulate various testing policies as well as interventions such as lockdown based on them. The interaction between the agents can take into account various communities and mobility patterns. A distinguishing feature of our framework is the presence of another `flu'-like illness with symptoms similar to COVID-19, that allows us to model the noise in selecting the pool of patients to be tested. We instantiate our model for the city of Bengaluru in India, using census data to distribute agents geographically, and traffic flow mobility data to model long-distance interactions and mixing. We use the simulation framework to compare the performance of three testing policies: Random Symptomatic Testing (RST), Contact Tracing (CT), and a new Location Based Testing policy (LBT). We observe that if a sufficient fraction of symptomatic patients come out for testing, then RST can capture the ground truth quite closely even with very few daily tests. However, CT consistently captures more positive cases. Interestingly, our new LBT, which is operationally less intensive than CT, gives performance that is comparable with CT. In another direction, we compare the efficacy of these three testing policies in enabling lockdown, and observe that CT flattens the ground truth curve maximally, followed closely by LBT, and significantly better than RST.

How Reliable are Test Numbers for Revealing the COVID-19 Ground Truth and Applying Interventions?

Covid-19: Lack of capacity led to halting of community testing in March, admits deputy chief medical officer

COVID-19, Australia: Epidemiology Report 17 (Fortnightly reporting period ending 24 May 2020).

Background: Rapid testing for an infection is paramount during a pandemic to prevent continued viral spread and excess morbidity and mortality. This study aimed to determine whether alternative testing strategies based on sample pooling can increase the speed and throughput of screening for SARS-CoV-2. Methods: A mathematical modelling approach was chosen to simulate six different testing strategies based on key input parameters (infection rate, test characteristics, population size, testing capacity etc.). The situations in five countries (US, DE, UK, IT and SG) currently experiencing COVID-19 outbreaks were simulated to reflect a broad variety of population sizes and testing capacities. The primary study outcome measurements that were finalised prior to any data collection were time and number of tests required; number of cases identified; and number of false positives. Findings: The performance of all tested methods depends on the input parameters, i.e. the specific circumstances of a screening campaign. To screen one tenth of each country's population at an infection rate of 1% - e.g. when prioritising frontline medical staff and public workers -, realistic optimised testing strategies enable such a campaign to be completed in ca. 29 days in the US, 71 in the UK, 25 in Singapore, 17 in Italy and 10 in Germany (ca. eight times faster compared to individual testing). When infection rates are considerably lower, or when employing an optimal, yet logistically more complex pooling method, the gains are more pronounced. Pool-based approaches also reduces the number of false positive diagnoses by 50%. Interpretation: The results of this study provide a clear rationale for adoption of pool-based testing strategies to increase speed and throughput of testing for SARS-CoV-2. The current individual testing approach unnecessarily wastes valuable time and resources.

Evaluation of Pool-based Testing Approaches to Enable Population-wide Screening for COVID-19

Background: Following a consistent decline in COVID-19-related deaths in the UK throughout May 2020, it is recognised that contact tracing will be vital to relaxing physical distancing measures. The increasingly evident role of asymptomatic and pre-symptomatic transmission means testing is central to control, but test sensitivity estimates are as low as 65%. Methods: We extend an existing UK-focused branching process model for contact tracing, adding diagnostic testing and refining parameter estimates to demonstrate the impact of poor test sensitivity and suggest mitigation methods. We also investigate the role of super-spreading events, providing estimates of the relationship between infections, cases detected and hospitalisations, and consider how tracing coverage and speed affects outbreak risk. Findings: Incorporating poor sensitivity testing into tracing protocols could reduce efficacy, due to false negative results impacting isolation duration. However, a 7-day isolation period for all negative-testing individuals could mitigate this effect. Similarly, reducing delays to testing following exposure has a negligible impact on the risk of future outbreaks, but could undermine control if negative-testing individuals immediately cease isolating. Even 100% tracing of contacts will miss cases, which could prompt large localised outbreaks if physical distancing measures are relaxed prematurely. Interpretation: It is imperative that test results are interpreted with caution due to high false-negative rates and that contact tracing is used in combination with physical distancing measures. If the risks associated with imperfect test sensitivity are mitigated, we find that contact tracing can facilitate control when the reproduction number with physical distancing, Rs, is less than 15.

An imperfect tool: COVID-19 'test & trace' success relies on minimising the impact of false negatives and continuation of physical distancing.

Background The outbreak of the novel coronavirus disease in 2019 (COVID-19) caused a major public health crisis worldwide and challenged healthcare systems across the six continents. The high infectivity of the disease led many governments to adopt strict regulations and measures with the aim of containing its spread. The purpose of this study is to assess the incidence, severity, and territorial expansion of COVID-19. Methods Data from the World Health Organization was screened, and COVID-19 situation reports were extracted from January 21 up till March 14 (inclusive). Our data included the total number of cases, total number of new cases, total number of cured cases, and total number of related deaths. Percentage change of cases over the days of our study were calculated using the Joinpoint regression, with a significance level set at greater than 0.05. Results The total number of COVID-19 cases reached 156,622, with 5,845 subsequent deaths. China, Italy, and Iran have the highest number of cases worldwide. During the first 22 days, the incidence rate of COVID-19 increased significantly to reach 1.81 cases per million persons (p<0.001). That was followed by a significant decrease over the next 11 days (p<0.001) to reach 0.071 cases per million persons. A steady rise then followed, which saw a significant increase in incidence rate to 1.429 cases per million persons (p<0.001). Percentages of death and cured cases varied across the different countries; nevertheless, death percentages have generally been decreasing since the start of the crisis. Conclusion Adopting precautionary regulations such as social isolation, increasing sanitation, and employing strict quarantine measures have proved to be beneficial in containing the virus. Further research needs to be conducted to help discover therapeutic modalities and improve outcomes.

An Epidemiological Study on COVID-19: A Rapidly Spreading Disease

We develop a simple analytical method to estimate the fraction of unreported infections in epidemics with a known epicenter and estimate the number of unreported COVID-19 infections in the US during the first half of March 2020. Our method utilizes the covariation in initial reported infections across US regions and the number of travelers to these regions from the epicenter, along with the results of a randomized testing study in Iceland. We estimate that 4%-14% (1.5%-10%) of actual infections had been reported in US up to March 16, accounting for an assumed reporting lag of 8 (5) days.

Estimating the Fraction of Unreported Infections in Epidemics with a Known Epicenter: an Application to COVID-19

Approximately 90 days of the SARS-CoV-2 (COVID-19) spreading originally from Wuhan, China, and across the globe has led to a widespread chain of events with imminent threats to the fragile relationship between community health and economic health. Despite near hourly reporting on this crisis, there has been no regular, updated, or accurate reporting of hospitalizations for COVID-19. It is known that many test-positive individuals may not develop symptoms or have a mild self-limited viral syndrome consisting of fever, malaise, dry cough, and constitutional symptoms. However some individuals develop a more fulminant syndrome including viral pneumonia, respiratory failure requiring oxygen, acute respiratory distress syndrome requiring mechanical ventilation, and in substantial fractions leading to death attributable to COVID-19. The pandemic is evolving in a clustered, non-inform fashion resulting in many hospitals with preparedness but few or no cases, and others that are completely overwhelmed. Thus, a considerable risk of spread when personal protection equipment becomes exhausted and a large fraction of mortality in those not offered mechanical ventilation are both attributable to a crisis due to maldistribution of resources. The pandemic is amenable to self-reporting through a mobile phone application that could obtain critical information on suspected cases and report on the results of self testing and actions taken. The only method to understand the clustering and the immediate hospital resource needs is mandatory, uniform, daily reporting of hospital censuses of COVID-19 cases admitted to hospital wards and intensive care units. Current reports of hospitalizations are delayed, uncertain, and wholly inadequate. This paper urges all the relevant stakeholders to take up self-reporting and reporting of hospitalizations of COVID-19 as an urgent task in combating this devastating pandemic.

Urgent need for individual mobile phone and institutional reporting of at home, hospitalized, and intensive care unit cases of SARS-CoV-2 (COVID-19) infection

Pandemics have a profound impact on our world, causing loss of life, affecting our culture and historically shaping our genetics. The response to a pandemic requires both resilience and imagination. It has been clearly documented that obtaining an accurate estimate and trends of the actual infection rate and mortality risk are very important for policy makers and medical professionals. One cannot estimate mortality rates without an accurate assessment of the number of infected individuals in the population. This need is also aligned with identifying the infected individuals so they can be properly treated, monitored and tracked. However, accurate estimation of the infection rate, locally, geographically and nationally is important independently. These infection rate estimates can guide policy makers at both state, national or world level to achieve a better management of risk to society. The decisions facing policy makers are very different during early stages of an emerging epidemic where the infection rate is low, middle stages where the rate is rapidly climbing, and later stages where the epidemic curve has flattened to a low and relatively sustainable rate. In this paper we provide relatively efficient pooling methods to both estimate infection rates and identify infected individuals for populations with low infection rates. These estimates may provide significant cost reductions for testing in rural communities, third world countries and other situations where the cost of testing is expensive or testing is not widely available. As we prepare for the second wave of the pandemic this line of work may provide new solutions for both the biomedical community and policy makers at all levels.

Rate Estimation and Identification of COVID-19 Infections: Towards Rational Policy Making During Early and Late Stages of Epidemics

Detection of SARS-CoV-2 infections to date has relied on RT-PCR testing. However, a failure to identify early cases imported to a country, bottlenecks in RT-PCR testing, and the existence of infections which are asymptomatic, sub-clinical, or with an alternative presentation than the standard cough and fever have resulted in an under-counting of the true prevalence of SARS-CoV-2. Here, we show how publicly available CDC influenza-like illness (ILI) outpatient surveillance data can be repurposed to estimate the detection rate of symptomatic SARS-CoV-2 infections. We find a surge of non-influenza ILI above the seasonal average and show that this surge is correlated with COVID case counts across states. By quantifying the number of excess ILI patients in March relative to previous years and comparing excess ILI to confirmed COVID case counts, we estimate the syndromic case detection rate of SARS-CoV-2 in the US to be less than 13%. If only 1/3 of patients infected with SARS-CoV-2 sought care, the ILI surge would correspond to more than 8.7 million new SARS-CoV-2 infections across the US during the three week period from March 8 to March 28. Combining excess ILI counts with the date of onset of community transmission in the US, we also show that the early epidemic in the US was unlikely to be doubling slower than every 4 days. Together these results suggest a conceptual model for the COVID epidemic in the US in which rapid spread across the US are combined with a large population of infected patients with presumably mild-to-moderate clinical symptoms. We emphasize the importance of testing these findings with seroprevalence data, and discuss the broader potential to use syndromic time series for early detection and understanding of emerging infectious diseases.

Using ILI surveillance to estimate state-specific case detection rates and forecast SARS-CoV-2 spread in the United States

The gold standard for COVID-19 diagnosis is detection of viral RNA in a reverse transcription PCR test. Due to global limitations in testing capacity, effective prioritization of individuals for testing is essential. Here, we devised a model that estimates the probability of an individual to test positive for COVID-19 based on answers to 9 simple questions regarding age, gender, presence of prior medical conditions, general feeling, and the symptoms fever, cough, shortness of breath, sore throat and loss of taste or smell, all of which have been associated with COVID-19 infection. Our model was devised from a subsample of a national symptom survey that was answered over 2 million times in Israel over the past 2 months and a targeted survey distributed to all residents of several cities in Israel. Overall, 43,752 adults were included, from which 498 self-reported as being COVID-19 positive. The model provides statistically significant predictions on held-out individuals and achieves a positive predictive value (PPV) of 46.3% at a 10% sensitivity. As our tool can be used online and without the need of exposure to suspected patients, it may have worldwide utility in combating COVID-19 by better directing the limited testing resources through prioritization of individuals for testing, thereby increasing the rate at which positive individuals can be identified and isolated.

Who should we test for COVID-19?A triage model built from national symptom surveys

In the early months of the pandemic, most reported cases and deaths due to COVID-19 occurred in high-income countries. However, insufficient testing could have led to an underestimation of true infections in many low- and middle-income countries. As confirmed cases increase, the ultimate impact of the pandemic on individuals and communities in low- and middle-income countries is uncertain. We therefore propose research in three broad areas as urgently needed to inform responses in low- and middle-income countries: transmission patterns of SARS-CoV-2, the clinical characteristics of the disease, and the impact of pandemic prevention and response measures. Answering these questions will require a multidisciplinary approach led by local investigators and in some cases additional resources. Targeted research activities should be done to help mitigate the potential burden of COVID-19 in low- and middle-income countries without diverting the limited human resources, funding, or medical supplies from response activities.

The need for COVID-19 research in low- and middle-income countries

Comparison of COVID-19 case numbers over time and between locations is complicated by limits to virologic testing confirm SARS-CoV-2 infection, leading to under-reporting of incidence, and by variations in testing capacity between locations and over time. The proportion of tested individuals who have tested positive (test positive proportion, TPP) can potentially be used to qualitatively assess the testing capacity of a location; a high TPP could provide evidence that too few people are tested, leading to more under-reporting. In this study we propose a simple model for testing in a population experiencing an epidemic of COVID-19, and derive an expression for TPP in terms of well-defined parameters in the model, related to testing and presence of other pathogens causing COVID-19 like symptoms. We use simulations to show situations in which the TPP is higher or lower than we expect based on these parameters, and the effect of testing strategies on the TPP. In our simulations, we find in the absence of dramatic shifts of testing practices in time or between spatial locations, the TPP is positively correlated with the incidence of infection. As a corollary, the TPP can be used to distinguish between a decline in confirmed cases due to decline in incidence (in which case TPP should decline) and a decline in confirmed cases due to testing constraints (in which case TPP should remain constant). We show that the proportion of tested individuals who present COVID-19 like symptoms (test symptomatic proportion, TSP) encodes similar information to the TPP but has different relationships with the testing parameters, and can thus provide additional information regarding dynamic changes in TPP and incidence. Finally, we compare data on confirmed cases and TPP from US states. We conjecture why states may have higher or lower TPP than average. We suggest that collection of symptom status and age/risk category of tested individuals can aid interpretation of changes in TPP and increase the utility of TPP in assessing the state of the pandemic in different locations and times.

The usefulness of SARS-CoV-2 test positive proportion as a surveillance tool

A key challenge in estimating the infection fatality rate (IFR) of COVID-19 is determining the total number of cases. The total number of cases is not known because not everyone is tested but also, more importantly, because tested individuals are not representative of the population at large. We refer to the phenomenon whereby infected individuals are more likely to be tested than non-infected individuals, as"preferential testing."An open question is whether or not it is possible to reliably estimate the IFR without any specific knowledge about the degree to which the data are biased by preferential testing. In this paper we take a partial identifiability approach, formulating clearly where deliberate prior assumptions can be made and presenting a Bayesian model, which pools information from different samples. Results suggest that when limited knowledge is available about the magnitude of preferential testing, reliable estimation of the IFR is still possible so long as there is sufficient"heterogeneity of bias"across samples.

Bayesian adjustment for preferential testing in estimating the COVID-19 infection fatality rate: Theory and methods

Covid-19: Lack of testing in Brazil is a "major failure," says MSF

The exact risk of dying from COVID-19 has remained elusive and a topic of debate. The observed case fatality rates of 46 different countries are hypothesized to be dependent on their testing rates. An analytical test to this hypothesis suggests that the case fatality rate of COVID-19 could be consistent to a certain degree across all countries and states. The current global fatality rate is estimated to be around 1% and expected to converge between 1-3% when the pandemic ends. This model can be helpful to estimate the true infection rate for individual countries.

The true case fatality of COVID19: An analytical solution

Given the low Covid-19 testing coverage in the country, this study tested whether the daily change in the number of new Covid-19 cases is due to increase (or decrease) in the number of tests done daily. We performed Granger causality test based on vector autoregressive models on Bangladesh case and test numbers between 8 March and 5 June 2020, using publicly available data. The test results show that the daily number of tests Granger-cause the number of new cases (p <0.001), meaning the daily number of new cases is perhaps due to an increase in test capacity rather than a change in the infection rates. From the results of this test we can infer that if the number of daily tests does not increase substantially, data on new infections will not give much information for understanding covid-19 infection dynamics in Bangladesh.

Is tracking and modeling Covid-19 infection dynamics for Bangladesh using daily data feasible?

Population density, behaviour and cultural habits strongly influence the spread of pathogens. Consequently, key epidemiological parameters may vary from country to country. Many estimates of SARS-CoV-2 and COVID-19 strongly depend on testing frequency and case definitions. The fatal cases due to SARS-CoV2 could be a more reliable parameter, since missing of deaths is less likely. We analysed the dynamics of new infection and death cases to estimate the daily reproduction numbers (Rt) and the effectiveness of control measures in the most affected European Countries and the US. In summary, calculating Rt based on the daily number of deaths as well as of new infections may lead to more reliable estimates than those based on infection cases alone, as death based Rt are expected to be less susceptible to testing bias or limited capacities.

Modest effects of contact reduction measures on the reproduction number of SARS-CoV-2 in the most affected European countries and the US

Background Intensive screening and testing for COVID-19 could facilitate early detection and isolation of infected persons and thereby control the size of the epidemic. It could also facilitate earlier and more targeted therapy. These factors could plausibly reduce attributable mortality which was the hypothesis tested in this study. Methods Linear regression was used to assess the country-level association between COVID-19 attributable mortality per 100 000 inhabitants (mortality/capita) and COVID-19 tests/capita (number of tests/100 000 inhabitants) controlling for the cumulative number of COVID-19 infections/100 000 inhabitants (cases/capita), the age of the epidemic (number of days between first case reported and 8 April), national health expenditure per capita and WHO world region. Results The COVID-19 mortality rate varied between 0.3 and 3110 deaths/100 000 inhabitants (median 30, IQR 8-105). The intensity of testing per 100 000 also varied considerably (median 21,970, IQR 2,735-89,095) as did the number of COVID-19 cases per 100 000 (median 1,600, IQR 340-4,760 cases/100 000). In the multivariate model, the COVID-19 mortality rate was negatively associated with tests/capita (Coef. -0.036, 95% CI -0.047- -0.025) and positively associated with cases/capita (Coef. 0.093, 95% CI 0.819- 1.034). Conclusions The results are compatible with the hypothesis that intensive testing and isolation could play a role in reducing COVID-10 mortality rates.

Intensive COVID-19 testing associated with reduced mortality - an ecological analysis of 108 countries

Confirmed cases in Australia notified up to 10 May 2020: notifications = 6,971; deaths = 98. The incidence of new cases of COVID-19 has reduced dramatically since a peak in mid-March. The reduction in international travel, social distancing measures and public health action have likely been effective in slowing the spread of the disease, in the Australian community. Cases of COVID-19 continue to be notified by jurisdictions, albeit at a slowed rate. Testing rates over the past week have increased markedly, with a very low proportion of people testing positive. These low rates of detection are indicative of low levels of COVID-19 transmission. It is important that testing rates and community adherence to public health measures remain high to support the continued suppression of the virus, particularly in vulnerable high-risk groups and settings. In the past reporting week new cases in Australia are mostly considered to be locally acquired, consistent with the drop in international travel. Most locally-acquired cases can be linked back to a known case or cluster. Although the proportion of locally-acquired cases has increased, the overall rate of cases, regardless of place of acquisition, continues to decrease. The crude case fatality rate in Australia remains low (1.4%), compared with the WHO reported global rate (6.9%). The low case fatality rate is likely reflective of high case detection and high quality of health care services in Australia. Deaths from COVID-19 in Australia have occurred predominantly among the elderly and those with comorbidities, with no deaths occurring in those under 40 years. The highest rate of COVID-19 continues to be among people aged 60-79 years, with a third of these cases associated with several outbreaks linked to cruise ships. The lowest rate of disease is in young children, a pattern reflected in international reports. Internationally, cases continue to increase, with some areas such as Brazil and India showing a dramatic rise in reported cases. Although some low-income countries have currently reported few cases, it is possible that this is due to limited diagnostic and public health capacity, and may not be reflective of disease occurrence.

COVID-19, Australia: Epidemiology Report 15 (Reporting week to 23:59 AEST 10 May 2020)

COVID-19 Waves: Importance of Accumulative Mortality per Million Inhabitants

Since the beginning of 2020, the coronavirus disease 2019 (COVID-19) has spread rapidly in the city of Wuhan, P.R. China, and subsequently, across the world. The swift spread of the virus is largely attributed to its stealth transmissions in which infected patients may be asymptomatic. Undetected transmissions present a remarkable challenge for the containment of the virus and pose an appalling threat to the public. An urgent question that has been asked by the public is"Should I be tested for COVID-19 if I am sick?". While different regions established their own criteria for screening infected cases, the screening criteria have been modified based on new evidence and understanding of the virus as well as the availability of resources. The shortage of test kits and medical personnel has considerably limited our ability to do as many tests as possible. Public health officials and clinicians are facing a dilemma of balancing the limited resources and unlimited demands. On one hand, they are striving to achieve the best outcome by optimizing the usage of the scant resources. On the other hand, they are challenged by the patients' frustrations and anxieties, stemming from the concerns of not being tested for COVID-19 for not meeting the definition of PUI (person under investigation). In this paper, we evaluate the situation from the statistical viewpoint by factoring into the considerations of the uncertainty and inaccuracy of the test, an issue that is often overlooked by the general public. We aim to shed light on the tough situation by providing evidence-based reasoning from the statistical angle, and we expect this examination will help the general public understand and assess the situation rationally. Most importantly, the development offers recommendations for physicians to make sensible evaluations to optimally use the limited resources for the best medical outcome.

COVID-19: Should We Test Everyone?