COMOKIT: A Modeling Kit to Understand, Analyze, and Compare the Impacts of Mitigation Policies Against the COVID-19 Epidemic at the Scale of a City.

Since its emergence in China, the COVID-19 pandemic has spread rapidly around the world. Faced with this unknown disease, public health authorities were forced to experiment, in a short period of time, with various combinations of interventions at different scales. However, as the pandemic progresses, there is an urgent need for tools and methodologies to quickly analyze the effectiveness of responses against COVID-19 in different communities and contexts. In this perspective, computer modeling appears to be an invaluable lever as it allows for the in silico exploration of a range of intervention strategies prior to the potential field implementation phase. More specifically, we argue that, in order to take into account important dimensions of policy actions, such as the heterogeneity of the individual response or the spatial aspect of containment strategies, the branch of computer modeling known as agent-based modeling is of immense interest. We present in this paper an agent-based modeling framework called COVID-19 Modeling Kit (COMOKIT), designed to be generic, scalable and thus portable in a variety of social and geographical contexts. COMOKIT combines models of person-to-person and environmental transmission, a model of individual epidemiological status evolution, an agenda-based 1-h time step model of human mobility, and an intervention model. It is designed to be modular and flexible enough to allow modelers and users to represent different strategies and study their impacts in multiple social, epidemiological or economic scenarios. Several large-scale experiments are analyzed in this paper and allow us to show the potentialities of COMOKIT in terms of analysis and comparison of the impacts of public health policies in a realistic case study.

Avian Influenza Human Infections at the Human-Animal Interface.

BACKGROUND: Avian influenza A viruses (AIVs) are among the most concerning emerging and re-emerging pathogens because of the potential risk for causing an influenza pandemic with catastrophic impact. The recent increase in domestic animals and poultry worldwide was followed by an increase of human AIV outbreaks reported. METHODS: We reviewed the epidemiology of human infections with AIV from the literature including reports from the World Health Organization, extracting information on virus subtype, time, location, age, sex, outcome, and exposure. RESULTS: We described the characteristics of more than 2500 laboratory-confirmed human infections with AIVs. Human infections with H5N1 and H7N9 were more frequently reported than other subtypes. Risk of death was highest among reported cases infected with H5N1, H5N6, H7N9, and H10N8 infections. Older people and males tended to have a lower risk of infection with most AIV subtypes, except for H7N9. Visiting live poultry markets was mostly reported by H7N9, H5N6, and H10N8 cases, while exposure to sick or dead bird was mostly reported by H5N1, H7N2, H7N3, H7N4, H7N7, and H10N7 cases. CONCLUSIONS: Understanding the profile of human cases of different AIV subtypes would guide control strategies. Continued monitoring of human infections with AIVs is essential for pandemic preparedness.