Coronavirus (COVID-19) Collection
Predicting the effective reproduction number of COVID-19: inference using human mobility, temperature, and risk awareness
The first confirmed case of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection was reported in Japan on January 15, 2020; since then, the transmission of coronavirus disease 2019 (COVID-19) has continuously affected the entire country. As the incidence of COVID-19 began to surge, a state of emergency was declared by the national government on April 16, requesting the voluntary reduction of physical contact, which likely helped to suppress the epidemic (Jung et al., 2021). However, resuming socioeconomic activities in late May led to a resurgence of cases.A hospital-related outbreak of SARS-CoV-2 associated with variant Epsilon (B.1.429) in Taiwan: transmission potential and outbreak containment under intensified contact tracing, January–February 2021
As of 9 March 2021, there have been fewer than 1,000 confirmed cases of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection in Taiwan, of which only 77 were locally acquired (Taiwan Centres for Disease Control, 2021). Following stringent border control measures, proactive contact tracing and case isolation, Taiwan's largest individual outbreak to date has been a hospital-related outbreak that involved 22 cases and occurred in January–February 2021. Despite successful containment of that outbreak, some aspects were concerning.Localized end-of-outbreak determination for coronavirus disease 2019 (COVID-19): examples from clusters in Japan
The coronavirus disease 2019 (COVID-19) pandemic has been the most fatal and disruptive biological disaster in recent history. As of 24 February 2021, COVID-19 had been diagnosed in over 113 million people and associated with more than 2.5 million deaths. Travel restrictions, school closures, cancellation of public events, and other public health and social measures implemented to curb disease transmission have deeply affected lives and livelihoods worldwide. In the midst of the disaster, mathematical modeling has served prominently in informing pandemic response as outbreaks have erupted and grown (Anderson et al., 2020; Ferguson et al., 2020), but the field can also provide insight into the transmission dynamics of outbreaks as they come to an end (Thompson et al., 2019; Lee and Nishiura, 2019).Estimation of the asymptomatic ratio of novel coronavirus infections (COVID-19)
The number of novel coronavirus (COVID-19) cases worldwide continues to grow, and the gap between reports from China and statistical estimates of incidence based on cases diagnosed outside China indicates that a substantial number of cases are underdiagnosed (Nishiura et al., 2020a). Estimation of the asymptomatic ratio—the percentage of carriers with no symptoms—will improve understanding of COVID-19 transmission and the spectrum of disease it causes, providing insight into epidemic spread. Although the asymptomatic ratio is conventionally estimated using seroepidemiological data (Carrat et al., 2008; Hsieh et al., 2014), the collection of these data requires significant logistical effort, time, and cost.Serial interval of novel coronavirus (COVID-19) infections
The epidemic of novel coronavirus (COVID-19) infections that began in China in late 2019 has rapidly grown and cases have been reported worldwide. An empirical estimate of the serial interval—the time from illness onset in a primary case (infector) to illness onset in a secondary case (infectee)—is needed to understand the turnover of case generations and transmissibility of the disease (Fine, 2003). Estimates of the serial interval can only be obtained by linking dates of onset for infector-infectee pairs, and these links are not easily established.
