This article synthesizes the results of case studies on the development of the coronavirus disease 2019 (COVID-19) pandemic and control measures by governments in 16 countries. When this work was conducted, only 6 months had passed since the pandemic began, and only 4 months since the first events were recognized outside of China. It was too early to draw firm conclusions about the effectiveness of measures in each of the selected countries; however, the authors present some efforts to identify and classify response and containment measures, country-by-country, for future comparison and analysis. There is a significant variety of policy tools and response measures employed in different countries, and while it is still hard to directly compare the different approaches based on their efficacy, it will definitely provide many inputs for the future data analysis efforts.

The ongoing pandemic of COVID-19 (Coronavirus Infectious Disease-2019) was first reported at the end of 2019 in Wuhan, China. On 30 January 2020, the WHO declared a Public Health Emergency for the novel coronavirus. On 11 March 2020, the WHO officially declared the COVID-19 outbreak as a pandemic. Due to the differences in population distribution, economic structure, degree of damage and other factors, the affected countries have introduced policies tailored to local conditions as a response to the pandemic, leading to different economic and social impacts. Considering the highly heterogeneous spreading of COVID-19 across regions, this paper takes a specific country (China) as a case study of the spread of the disease and national intervention models for the COVID-19 pandemic. The research period of this article is from 17 December to 26 April 2020, because this time period basically covered the important time nodes of the epidemic in China from animal-to-human transmission, limited human-to-human transmission, epidemic to gradual control. This study is useful for comparing the effectiveness of different interventions at various stages of epidemic development within the same country and can also promote the comparison of the epidemic response interventions of different countries. Based on the conclusions of the model simulation, this article evaluates the dual impact of the epidemic on people’s wellbeing and the economy.

We have developed a mathematical model and stochastic numerical simulation for the transmission of COVID-19 and other similar infectious diseases that accounts for the geographic distribution of population density, detailed down to the level of location of individuals, and age-structured contact rates. Our analytical framework includes a surrogate model optimization process to rapidly fit the parameters of the model to the observed epidemic curves for cases, hospitalizations, and deaths. This toolkit (the model, the simulation code, and the optimizer) is a useful tool for policy makers and epidemic response teams, who can use it to forecast epidemic development scenarios in local settings (at the scale of cities to large countries) and design optimal response strategies. The simulation code also enables spatial visualization, where detailed views of epidemic scenarios are displayed directly on maps of population density. The model and simulation also include the vaccination process, which can be tailored to different levels of efficiency and efficacy of different vaccines. We used the developed framework to generate predictions for the spread of COVID-19 in the canton of Geneva, Switzerland, and validated them by comparing the calculated number of cases and recoveries with data from local seroprevalence studies.

The recent lifting of COVID-19 related restrictions in Switzerland causes uncertainty about the future of the epidemic. We developed a compartmental model for SARS-CoV-2 transmission in Switzerland and projected the course of the epidemic until the end of year 2020 under various scenarios. The model was age-structured with three categories, children (0-17), adults (18-64) and seniors (65- years). Lifting all restrictions according to the plans disclosed by the Swiss federal authorities by mid-May resulted in a rapid rebound in the epidemic, with the peak expected in July. Measures equivalent to at least 90% reduction in all contacts were able to eradicate the epidemic; 56% reduction in contacts could keep the intensive care unit occupancy under the critical level, and delay the next wave until October. Scenarios where strong contact reductions were only applied in selected age groups could not suppress the epidemic, increasing the risk of a next wave in July, and another stronger wave in September. Future interventions need to cover all age groups to keep the SARS-CoV-2 epidemic under control.

In this paper we investigate a susceptible-infected-susceptible (SIS) epidemic model describing data dissemination in opportunistic networks with heterogeneous setting of transmission parameters. We obtained the estimation of the final epidemic size assuming that amount of data transferred between network nodes possesses a Pareto distribution, implying scale-free properties. In this context, more heterogeneity in susceptibility means the less severe epidemic progression, and, on the contrary, more heterogeneity in infectivity leads to more severe epidemics -assuming that the other parameter (either heterogeneity or susceptibility) stays fixed. The results are general enough and can be useful in a broader context of epidemic theory, e.g. for estimating the progression for diseases with no significant acquired immunityin the cases where Pareto distribution holds.