Infection Fatality Rate and Infection Attack Rate of COVID-19 in South American Countries (preprint)

Background: The ongoing COVID-19 pandemic hit South America badly with two waves. Different COVID-19 variants have been storming across the region, leading to more severe infections and deaths even in places with high vaccination coverages. Methods: We use the start-of-the-art iterated filtering likelihood-based inference disease modelling framework. We modify the classical susceptible-exposed-infectious-recovered model with a time-varying transmission rate, and additional delayed class and vaccinations to reported COVID-19 deaths in 12 South American countries with the highest COVID-19 mortalities. Results: We yield biologically reasonable estimates for the infection fatality rate (IFR), the infection attack rate (IAR) and time-varying transmission rate. We observe that the severity, the dynamical patterns of the deaths and the time-varying transmission rates among the countries are highly heterogeneous. Further, our analysis of the model with vaccination highlights that increasing the vaccination rate could effectively suppress the pandemics in South America. Conclusion: This study reveals the possible mechanism behind the two waves of COVID-19 in South America. We observe reductions in the transmission rate corresponding to each wave plausibly due to improvement in nonpharmaceutical interventions (NPIs) measures and human protective behavior reaction to recent deaths. Thus, strategies coupling social distancing and vaccination could substantially suppress the mortality rate of COVID-19 in South America.

The heterogeneous severity of COVID-19 in African countries: A modeling approach (preprint)

Background: The COVID-19 pandemic has had a considerable impact on global health and economics. The impact in African countries has not been investigated through fitting epidemic models to the reported COVID-19 deaths.Method: We downloaded data for the twelve most affected countries with the highest cumulative COVID-19 deaths to estimate the time-varying basic reproductive number (R0(t)) and infection attack rate (IAR). We developed a simple epidemic model and fitted the model to reported COVID-19 deaths in twelve African countries using iterated filtering and allowing a flexible transmission rate.Results: We observed high heterogeneity in the case-fatality rate across countries, which may be due to different reporting or testing efforts. South Africa, Tunisia, and Libya were affected most strongly, exhibiting a relatively higher(R0(t)) and infection attack rate.Conclusion: To effectively control the spread of COVID-19 epidemics in Africa, there is a need to consider other mitigation strategies (such as improvements in socioeconomic well-being, healthcare systems, the water supply, and awareness campaigns).

Heterogeneous Severity of COVID-19 in African Countries: A Modeling Approach (preprint)

Background: The COVID-19 pandemic has caused tremendous impact on global health and economics. The impact in African countries has not been investigated through fitting epidemic model to the reported COVID-19 deaths. Method: We downloaded data for the twelve most-affected countries with the highest cumulative COVID-19 deaths to estimate the time-varying effective reproduction number (B) and infection attack rate (IAR). We developed a simple epidemic model and fitted the model to reported COVID-19 deaths in 12 African countries, using iterated filtering and allowing flexible transmission rate. Results: : We found high heterogeneity in the case-fatality rate across countries, which may be due to different reporting or testing efforts. We found that South Africa, Tunisia, and Libya were hit hardest with a relatively higher a and infection attack rate Conclusion: To effectively control the spread of COVID-19 epidemics in Africa, there is a need to consider other mitigation strategies (such as improvement in socio-economic wellbeing, health care system, water supply, awareness campaigns).

Preliminary estimation of the basic reproduction number of novel coronavirus (2019-nCoV) in China, from 2019 to 2020: A data-driven analysis in the early phase of the outbreak (preprint)

BackgroundsAn ongoing outbreak of a novel coronavirus (2019-nCoV) pneumonia hit a major city of China, Wuhan, December 2019 and subsequently reached other provinces/regions of China and countries. We present estimates of the basic reproduction number, R0, of 2019-nCoV in the early phase of the outbreak. MethodsAccounting for the impact of the variations in disease reporting rate, we modelled the epidemic curve of 2019-nCoV cases time series, in mainland China from January 10 to January 24, 2020, through the exponential growth. With the estimated intrinsic growth rate ({gamma}), we estimated R0 by using the serial intervals (SI) of two other well-known coronavirus diseases, MERS and SARS, as approximations for the true unknown SI. FindingsThe early outbreak data largely follows the exponential growth. We estimated that the mean R0 ranges from 2.24 (95%CI: 1.96-2.55) to 3.58 (95%CI: 2.89-4.39) associated with 8-fold to 2-fold increase in the reporting rate. We demonstrated that changes in reporting rate substantially affect estimates of R0. ConclusionThe mean estimate of R0 for the 2019-nCoV ranges from 2.24 to 3.58, and significantly larger than 1. Our findings indicate the potential of 2019-nCoV to cause outbreaks.