Skip to main content Skip to local navigation

Towards Epidemiological Intelligence: modeling based analytics combating COVID-19

Towards Epidemiological Intelligence: modeling based analytics combating COVID-19

Speaker: Xiaonong Zhou, Professor, Director National Institute of Parasitic Diseases at Chinese Center of Disease Control and Prevention & Chinese Center for Tropical

Xiao-Nong Zhou is Director of the National Institute of Parasitic Diseases at Chinese Center for Disease Control and Prevention & Chinese Center for Tropical Diseases Research, and WHO Collaborating Centre for Tropical Diseases, based in Shanghai, China. He has been the Vice Chair of the Chinese Ministry of Health Expert Committee on Disease Control and Chair for the Sub-group of Parasitic Diseases and Schistosomiasis since 2006. He is serving the member of the Expert Consultancy Committee for the Healthy China Action and Promotion Committee of State Council; Chair of the Society of Medical Parasitology, Chinese Preventive Medicine Association; Vice Chair of the Society of Global Health, Chinese Preventive Medicine Association.

Dr Zhou is a leading expert in the research and control of parasitic diseases, with over 30 years’ experience in the field. In particularly, his interests have been on research for pathogen-host interaction and spatial epidemiology on parasitic diseases leading to innovative strategy to support national control progrmme. With great efforts on implementation research, his contribution on establishment of surveillance-response systems for parasitic diseases at national level has promoted the elimination of parasitic diseases significantly. He also has had long-term cooperation experiences with the World Health Organization and serves as member of international expert committees, such as the Chair of the WPRO Review Group on Neglected Tropical Diseases, member of the WHO-TDR Strategic and Technical Advisory Committee, and member of the WHO Strategic Advisory Group on Malaria Eradication, etc.

Dr Zhou is the Editor-in-Chief of Infectious Diseases of Poverty, the Associate Editor of PLOS Neglected Tropical Diseases, and a board member of Parasites and Vectors journals. He has published more than 300 articles in peer-reviewed journals and more than six academic books. He has been a principal investigator for more than 20 national and international research projects.


As the COVID-19 pandemic spreads rapidly around the globe, it becomes a challenging task to maintain core malaria control service while protecting health workers against COVID-19 transmission in malaria-endemic regions. The emergence of the novel COVID-19 has led to public health emergencies and disrupted malaria control services in many malaria-endemic regions.

In order to timely mitigate the impact of COVID-19 pandemic, there is an urgent need to understand the underlying transmission patterns among different populations throughout different phases of the COVID-19 outbreak. By doing so, we develop a computational model to reveal the interactions in terms of the social contact patterns among the population of different age-groups, by considering four representative settings of social contacts that may cause the disease spread. Such a model can provide insights into what may have happened retrospectively and what can be anticipated prospectively of the disease outbreak, so as to further address a series of important questions that follow, such as how future risks and trends in different regions may evolve, how effective can different intervention strategies be in controlling the outbreak, and what may happen if people gradually return to schools and workplaces.

Some countries in sub-Saharan Africa had suspended mass insecticide-treated net (ITN) campaigns, which are considered the most important measure for malaria intervention and control across Africa in the last two decades. A cessation of distribution of INTs may result in an increase in human biting rate, and further the number of malaria infections. In this case, there is an urgent need to assess the negative impact of pandemic COVID-19 on the risk of malaria transmission in malaria-endemic countries in Africa. In view of this, we present a data-driven analytic method to assess the potential deterioration of malaria transmission risk because of the cessation or disruption of distribution of ITNs in malaria-endemic countries in Africa during the COVID-19 pandemic.