Protecting health professionals is crucial to maintain a functioning healthcare system. However, little is known about the risk factors affecting healthcare personnel during the COVID-19 pandemic. We implemented a weekly testing regime on the cohort to identify pre- and asymptomatic individuals at a department of Bern University Hospital among a cohort of 330 healthcare professionals. We have developed a mathematical model of SARS-CoV-2 transmission that integrates the infection dynamics among the cohort. We used our model to study how regular testing and a shift work protocol are effective in preventing transmission of COVID-19 infection at work, and compared both strategies in terms of workforce availability and cost-effectiveness. We showed that case incidence among health workers is higher than would be explained solely by community infection. Furthermore, while both strategies are effective in preventing nosocomial transmission, regular testing allows work productivity to be maintained while keeping implementation costs lower than shift work.
Early-warning signals are widely used in many fields to anticipate a critical threshold prior to reaching it. A systems undergoes the phenomenon known as critical slowing down as it crosses through a bifurcation. Theory predicts that fluctuations away from the mean will recover more slowly as the system approaches a critical transition. This is key in infectious disease modelling to assess when the basic reproduction number is reduced below the threshold of one. Theoretical advances have shown indicators of critical transitions in epidemiology, such as measuring the rising lag-1 autocorrelation in synthetic disease data. An effective early-warning signal would be able to predict an impending critical transition of this type with a suitable 'lead time' in order to act on the current path of the disease.We validate several empirical studies which offer lead time predictions for ecological and infectious diseases systems when using this theory practice. Our work highlights several challenges when applying lead time methodologies to simulated models. We find poor specificity, falsely reporting a critical transition in simulations at steady state.In this talk we present an extension to these methods and our results show promising potential for calculating early-warning signals of elimination on real-world noisy data.
Basque Center for Applied Mathematics
"Modeling COVID19 in the Basque Country: from introduction to control"
In March 2020, a multidisciplinary task force (so-called Basque Modelling Task Force, BMTF) was created to assist the Basque health managers and Government during the COVID-19 responses. BMTF is a modelling team, working on different approaches, including stochastic processes, statistical methods and artificial intelligence. Here we describe the efforts and challenges to develop a flexible modeling framework able to describe the dynamics observed for the tested positive cases, including the modelling development steps. The results obtained by a new stochastic SHARUCD model framework are presented. Our models differentiate mild and asymptomatic from severe infections prone to be hospitalized and were able to predict the course of the epidemic, providing important projections on the national health system's necessities during the increased population demand on hospital admissions. Short and longer-term predictions were tested with good results adjusted to the available epidemiological data. We have shown that the partial lockdown measures were effective and enough to slow down disease transmission in the Basque Country. This framework is now being used to monitor disease transmission while the country lockdown was gradually lifted, with insights to specific programs for a general policy of “social distancing” and home quarantining.
University of Georgia
" Predicting reservoirs of mosquito-borne zoonoses: Modelling interactions between temperature and pace of host life history"
The “pace” of host life history is an important driver of pathogen transmission dynamics in wildlife populations. Populations of species with a faster pace-of-life (generally associated with more frequent reproduction and a shorter lifespan) are often the most competent reservoirs for zoonoses and present a greater risk of spillover to human populations. However, the role of pace in systems of mosquito-borne pathogen transmission, where temperature also plays a crucial role in the population dynamics of mosquitoes, has not been previously studied. By considering a compartmental model of pathogen transmission, which incorporates important features of mosquito and vertebrate life history, we investigate how temperature and pace interact to determine zoonotic potential in these systems, measured through the basic reproduction number. We determine that the relationship between zoonotic potential, pace, and temperature as predicted by the “pace-of-life” and “warmer-means-sicker” hypotheses occurs only in some cases, depending on how host traits vary with the pace of their life history. Overall, incorporating realistic assumptions about mosquito-host contact rates and variations in host life history, pace and temperature interact in complex ways to drive transmission dynamics.