Department of Software Engineering, University of Malaya, Kuala Lumpur, 50603, Malaysia.
Department of Health Services Policy and Management, Arnold School of Public, Health, Columbia, 29208, SC, United States.
Global Health. 2022 Mar 28;18(1):37. doi: 10.1186/s12992-022-00827-3.
Uncertainties surrounding the 2019 novel coronavirus (COVID-19) remain a major global health challenge and requires attention. Researchers and medical experts have made remarkable efforts to reduce the number of cases and prevent future outbreaks through vaccines and other measures. However, there is little evidence on how severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection entropy can be applied in predicting the possible number of infections and deaths. In addition, more studies on how the COVID-19 infection density contributes to the rise in infections are needed. This study demonstrates how the SARS-COV-2 daily infection entropy can be applied in predicting the number of infections within a given period. In addition, the infection density within a given population attributes to an increase in the number of COVID-19 cases and, consequently, the new variants.
Using the COVID-19 initial data reported by Johns Hopkins University, World Health Organization (WHO) and Global Initiative on Sharing All Influenza Data (GISAID), the result shows that the original SAR-COV-2 strain has R<1 with an initial infection growth rate entropy of 9.11 bits for the United States (U.S.). At close proximity, the average infection time for an infected individual to infect others within a susceptible population is approximately 7 minutes. Assuming no vaccines were available, in the U.S., the number of infections could range between 41,220,199 and 82,440,398 in late March 2022 with approximately, 1,211,036 deaths. However, with the available vaccines, nearly 48 Million COVID-19 cases and 706, 437 deaths have been prevented.
The proposed technique will contribute to the ongoing investigation of the COVID-19 pandemic and a blueprint to address the uncertainties surrounding the pandemic.
2019 年新型冠状病毒（COVID-19）带来的不确定性仍是全球主要卫生挑战，需要加以关注。研究人员和医学专家通过疫苗和其他措施，为降低病例数量和预防未来疫情爆发付出了巨大努力。然而，关于严重急性呼吸系统综合征冠状病毒 2（SARS-CoV-2）感染熵如何应用于预测可能的感染和死亡人数，证据有限。此外，还需要更多研究来了解 COVID-19 感染密度如何导致感染人数增加。本研究展示了如何应用 SARS-CoV-2 日感染熵来预测特定时间段内的感染人数。此外，特定人群内的感染密度会导致 COVID-19 病例增加，进而导致新变种出现。
使用约翰霍普金斯大学、世界卫生组织（WHO）和全球流感共享倡议数据库（GISAID）报告的 COVID-19 初始数据，结果表明原始 SARS-CoV-2 株的 R<1，美国（U.S.）的初始感染增长率熵为 9.11 比特。接近这一数值，受感染者在易感人群中传染他人的平均感染时间约为 7 分钟。假设没有疫苗可用，美国 2022 年 3 月下旬的感染人数可能在 41,220,199 到 82,440,398 之间，死亡人数约为 1,211,036 人。然而，有了可用的疫苗，近 4800 万 COVID-19 病例和 706437 例死亡得到了预防。
该方法将有助于对 COVID-19 大流行的持续调查，并为解决大流行相关不确定性提供蓝图。
