ABSTRACT
Targeted surveillance allows public health authorities to implement testing and isolation strategies when diagnostic resources are limited. When transmission patterns are determined by social contact rates, the consideration of social network topologies in testing schemes is one avenue for targeted surveillance, specifically by prioritizing those individuals likely to contribute disproportionately to onward transmission. Yet, it remains unclear how to implement such surveillance and control when network data is unavailable, as is often the case in resource-limited settings. We evaluated the efficiency of a testing strategy that targeted individuals based on their degree centrality on a social network compared to a random testing strategy in the context of low testing capacity. We simulated SARS-CoV-2 dynamics on two contact networks from rural Madagascar and measured the epidemic duration, infection burden, and tests needed to end the epidemics. In addition, we examined the robustness of this approach when individuals true degree centralities were unknown and were instead estimated via readily-available socio-demographic variables (age, gender, marital status, educational attainment, and household size). Targeted testing reduced the infection burden by between 5 - 50% at low testing capacities, while requiring up to 28% fewer tests than random testing. Further, targeted tested remained more efficient when the true network topology was unknown and prioritization was based on socio-demographic characteristics, demonstrating the feasibility of this approach under realistic conditions. Incorporating social network topology into epidemic control strategies is an effective public health strategy for health systems suffering from low testing capacity and can be implemented via socio-demographic proxies when social networks are unknown. *French abstract available in Supplemental Materials
ABSTRACT
Since the emergence of SARS-CoV-2, governments around the World have implemented a combination of public health responses based on non-pharmaceutical interventions (NPIs), with significant social and economic consequences. Though most European countries have overcome the first epidemic wave, it remains of high priority to quantify the efficiency of different NPIs to inform preparedness for an impending second wave. In this study, combining capture-recapture methods with Bayesian inference in an age-structured mathematical model, we use a unique European dataset compiled by the European Centre for Disease Control (ECDC) to quantify the efficiency of 24 NPIs and their combinations (referred to as public health responses, PHR) in reducing SARS-Cov-2 transmission rates in 32 European countries. Of 166 unique PHR tested, we found that median decrease in viral transmission was 74%, which is enough to suppress the epidemic. PHR efficiency was positively associated with the number of NPIs implemented. We found that bans on mass gatherings had the largest effect among NPIs, followed by school closures, teleworking, and stay home orders. Partial implementation of most NPIs resulted in lower than average response efficiency. This first large-scale estimation of NPI and PHR efficiency against SARS-COV-2 transmission in Europe suggests that a combination of NPIs targeting different population groups should be favored to control future epidemic waves.
ABSTRACT
Due to the COVID-19 pandemic, many countries have implemented a complete lockdown of their population that may not be sustainable for long. To identify the best strategy to replace this full lockdown, sophisticated models that rely on mobility data have been developed. In this study, using the example of France as a case-study, we develop a simple model considering contacts between age classes to derive the general impact of partial lockdown strategies targeted at specific age groups. We found that epidemic suppression can only be achieved by targeting isolation of young and middle age groups with high efficiency. All other strategies tested result in a flatter epidemic curve, with outcomes in (e.g. mortality and health system over-capacity) dependent of the age groups targeted and the isolation efficiency. Targeting only the elderly can decrease the expected mortality burden, but in proportions lower than more integrative strategies involving several age groups. While not aiming to provide quantitative forecasts, our study shows the benefits and constraints of different partial lockdown strategies, which could help guide decision-making.
