Predicting COVID-19 incidence in war-torn Afghanistan: A timely response is required!
Authors: Usman Ayub Awan, Muhammad Wasif Malik, Muhammad Imran Khan, Aamer Ali Khattak, Haroon Ahmed, Usman Hassan, Humera Qureshi, Muhammad Sohail Afzal
Journal: Journal of Infection (2021)

We read with great interest the article entitled "A quick prediction tool for unfavorable outcome in COVID-19 inpatients: Development and internal validation" by Salto-Alejandre et al.1Salto-Alejandre S. Roca-Oporto C. Martín-Gutiérrez G. Avilés M.D. Gómez-González C. Navarro-Amuedo M.D. et al.A quick prediction tool for unfavourable outcome in COVID-19 inpatients: development and internal validation.J Infect. 2021; 82: e11-e15Abstract Full Text Full Text PDF PubMed Scopus (14) Google Scholar Authors of this article forecast the outcomes of COVID-19. The upsurge in COVID-19 cases, along with health issues in the war-torn country, stoked fears of a disaster amid a still-raging conflict. As a result, this paper will focus on the future prediction of COVID-19 cases in the war-torn country of Afghanistan, providing them with additional insight for taking necessary preventive measures. In Afghanistan, it's important to remember that both natural and human cause the emergence of new diseases. Military conflicts dominated by war and regional tribal and religious struggle have been significantly influenced by wrecking infrastructure and collapsing healthcare systems, among other human activities. People who dwell in the conflicted area will face many short- and long-term impacts. Shortage of facilities, which is generally the case inside congested refugee settlements with sanitation difficulties, greater exposure to disease vectors, and collapse of healthcare infrastructure, contribute to disastrous outcomes when large populations are evacuated.2Morse S. Factors and determinants of disease emergence.Rev Sci Tech Off Int Des Épizoot. 2004; 23: 443-452Crossref PubMed Scopus (99) Google Scholar In war-affected country Afghanistan, the COVID-19 pandemic wreaks havoc on the previously fragile public health systems, dramatically undermining the government. In recent weeks, the worrying resurgence has seen an upsurge in COVID-19 cases, with the overall number of cases surpassing 0.15 million and the death toll over 7000 so far.3Emergency Situational Updates. 2021. https://www.who.int/publications/m/item/weekly-epidemiological-update-on-covid-19—24-august-2021. Accessed 25-08-2021.Google Scholar,4World Health Organization (WHO). WHO Coronavirus (COVID-19) dashboard. 2021.Google Scholar Estimating newly reported cases and expected future ones are essential to sustain the health system's allocation of demand and resources. In both short and long-term situations, well-founded statistical modeling techniques are required to forecast the extent and kind of containment actions taken.5Zhang S. Diao M. Yu W. Pei L. Lin Z. Chen D. Estimation of the reproductive number of novel coronavirus (COVID-19) and the probable outbreak size on the diamond princess cruise ship: a data-driven analysis.Int J Infect Dis. 2020; 93: 201-204Abstract Full Text Full Text PDF PubMed Scopus (432) Google Scholar The Exponential Smoothing (ES) model is a widely use forecasting model that only assigns positive weights to past and current values. This method is appropriate for forecasting time series with no trend or seasonal pattern. The mean of the ES may be altering slowly over time. It is weighted average measures with weights decreasing exponentially as values become older.6Singh K. Shastri S. Bhadwal A.S. Kour P. Kumari M. Sharma A. et al.Implementation of exponential smoothing for forecasting time series data.Int J Sci Res Comput Sci Appl Manag Stud. 2019; 8: 1-4Google Scholar,7Gelper S. Fried R. Croux C. Robust forecasting with exponential and holt–winters smoothing.J Forecast. 2010; 29: 285-300Google Scholar The ES formula is given below: Ft = αAt−1 + (1−α) Ft−1, t > 0 (Where α is the smoothing factor, and 0 < α < 1). Figs. 1,2 illustrates an estimate of COVID-19 cases and deaths for the following 12 months using month-by-month data (Feb-2020 to Aug-2021). The results of the ES model (Holt's linear trend) indicate that the number of COVID-19 infected cases each month might reach 31,757 (CI 95%: 8017–55,497), while the number of deaths may reach 1572 (CI 95%: 493–2651). According to the anticipated figure, the number of infected cases and deaths will surge over the forthcoming 12 months. Prediction errors were used to assess the model's validity. We evaluated the model's accuracy using the mean absolute error (MAE) and R2 parameters. The ES (Holt's linear trend) month-by-month registered cases model with R-squared (0.62) and MAE (6993.21), as well as the model of the month-by-month death with R-squared (0.65) and MAE (334.79), have been validated and successfully predicted.Fig. 2Prediction of the mortality rate will increase over the forthcoming 12 months. The accuracy and validity of this Exponential Smoothing (ES) (Holt's linear trend) month-by-month registered deaths model with R-squared (0.65) and MAE (334.79).View Large Image Figure ViewerDownload Hi-res image Download (PPT) Afghanistan is currently experiencing one of the world's serious medical emergencies, owing to the country's current political unrest state. According to the World Health Organization (WHO), conflicts, environmental catastrophes, and civil and political unrest have resulted in the need for emergency care for more than 3.7 million Afghans.8Afghanistan-Situation reports. 2020. http://www.emro.who.int/afg/afghanistan-infocus/situation-reports.html. Accessed 19-08-2021.Google Scholar Statistics will assist public health practitioners and officials in understanding the locations, timings, and demographics of newly diagnosed cases. Public health responses to the COVID-19 outbreak and future pandemics may be impacted by accurate and timely infectious disease forecasts, which can help decide the most effective preventative and mitigation methods and when they should be deployed. To prevent the spread of disease, these data can also be utilized to strengthen public health advocacy and the nature and breadth of government programs. Forecasting COVID-19 cases are crucial to ensure healthcare coverage for Afghan people with unknown infectious diseases; otherwise, this pandemic will be more devastating in this impoverished region. There is no role of any funding agency related to this study.