The Coronavirus Disease 2019 ( COVID-19 ), has infected more than 170 ,000 people globally and resulted in over 6 ,000 deaths over a three-month period. Contrary to mainstream views, there is growing literature on pre-symptomatic and asymptomatic individuals contributing significantly to the disease outbreak . Several recent studies on serial infection yielded a mean interval of around 4 days, shorter than the mean symptom onset time of 5-7 days, and negative serial interval in more than 10\% of the cases. These observations highlight the urgent need to quantify the pre-symptomatic transmission for adequate global response. In this paper, we develop an epidemic model driven by pre-symptomatic transmission. Within the model construct, the infectiousness of a viral carrier on a given day is identified with their symptom onset probability , which we characterise through extensive studies of the clinical literature. The well-known Lotka-Euler estimating equation can then be invoked to relate the daily growth rate \lambda of the epidemic with the basic reproduction number R_0 . We applied the disease spreading model to epidemic development in affected provinces across China following the Wuhan lockdown on January 23, 2020. The remarkable three-phase universal pattern can be well-captured by the model. Despite its simplicity, the model allows synthesis of data from diverse sources to form a quantitative understanding of key mechanisms that drive or contain the disease spreading, and to make informed decisions to bring the pandemic under control .
The COVID-19 has infected more than 462 ,000 people globally and resulted in over 20 ,000 deaths as of March 26, 2020. Swift government response to contain the outbreak requires accurate and continuous census of the infected population, particularly with regard to viral carriers without severe symptoms. We take on this task by converting the symptom onset time distribution , which we calibrated, into the percentage of the latent, pre-symptomatic and symptomatic groups through a novel mathematical procedure. We then estimate the reduction of the basic reproduction number R_0 under specific disease control practices such as contact tracing, testing and social distancing. Finally, in light of our model findings, we analyze the bell-shaped curves of epidemic evolution from various affected regions, and recommend measures to steer the pandemic to its final descent at a universal decay rate of -0.32/day .
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Coronavirus Disease 2019 (
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),
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170
462
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over a three-month period. Contrary to mainstream views, there is growing literature on pre-symptomatic and asymptomatic individuals contributing significantly to the disease outbreak . Several recent studies on serial infection yielded a mean interval of around 4 days, shorter than the mean symptom onset time of 5-7 days, and negative serial interval in more than 10\% of the cases. These observations highlight the urgent need to quantify the pre-symptomatic transmission for adequate global response. In this paper, we develop an epidemic model driven by pre-symptomatic transmission. Within the model construct, the infectiousness of a viral carrier on a given day is identified with their symptom onset probability
as of March 26, 2020. Swift government response to contain the outbreak requires accurate and continuous census of the infected population, particularly with regard to viral carriers without severe symptoms. We take on this task by converting the symptom onset time distribution
R
characterise through extensive studies of the clinical literature. The well-known Lotka-Euler estimating equation can then be invoked to relate the daily growth rate \lambda of the epidemic with the
calibrated, into the percentage of the latent, pre-symptomatic and symptomatic groups through a novel mathematical procedure. We then estimate the reduction of the
R
. We applied the disease spreading model to epidemic development in affected provinces across China following the Wuhan lockdown on January 23, 2020. The remarkable three-phase universal pattern can be well-captured by the model. Despite its simplicity, the model allows synthesis of data from diverse sources to form a quantitative understanding of key mechanisms that drive or contain the disease spreading, and to make informed decisions to bring the pandemic under control
under specific disease control practices such as contact tracing, testing and social distancing. Finally, in light of our model findings, we analyze the bell-shaped curves of epidemic evolution from various affected regions, and recommend measures to steer the pandemic to its final descent at a universal decay rate of -0.32/day