Reducing societal impacts of SARS-CoV-2 interventions through subnational implementation (preprint)

To curb the initial spread of SARS-CoV-2, many countries relied on nation-wide implementation of non-pharmaceutical intervention measures, resulting in substantial socio-economic impacts. Potentially, subnational implementations might have had less of a societal impact, but comparable epidemiological impact. Here, using the first COVID-19 wave in the Netherlands as a case in point, we address this issue by developing a high-resolution analysis framework that uses a demographically-stratified population and a spatially-explicit, dynamic, individual contact-pattern based epidemiology, calibrated to hospital admissions data and mobility trends extracted from mobile phone signals and Google. We demonstrate how a subnational approach could achieve similar level of epidemiological control in terms of hospital admissions, while some parts of the country could stay open for a longer period. Our framework is exportable to other countries and settings, and may be used to develop policies on subnational approach as a better strategic choice for controlling future epidemics.

Uncertainty quantification and sensitivity analysis of COVID-19 exit strategies in an individual-based transmission model (preprint)

Many countries are currently dealing with the COVID-19 epidemic and are searching for an exit strategy such that life in society can return to normal. To support this search, computational models are used to predict the spread of the virus and to assess the efficacy of policy measures before actual implementation. The model output has to be interpreted carefully though, as computational models are subject to uncertainties. These can stem from, e.g., limited knowledge about input parameters values or from the intrinsic stochastic nature of some computational models. They lead to uncertainties in the model predictions, raising the question what distribution of values the model produces for key indicators of the severity of the epidemic. Here we show how to tackle this question using techniques for uncertainty quantification and sensitivity analysis. We assess the uncertainties and sensitivities of four exit strategies implemented in an agent-based transmission model with geographical stratification. The exit strategies are termed Flattening the Curve, Contact Tracing, Intermittent Lockdown and Phased Opening. We consider two key indicators of the ability of exit strategies to avoid catastrophic health care overload: the maximum number of prevalent cases in intensive care (IC), and the total number of IC patient-days in excess of IC bed capacity. Our results show that uncertainties not directly related to the exit strategies are secondary, although they should still be considered in comprehensive analysis intended to inform policy makers. The sensitivity analysis discloses the crucial role of the intervention uptake by the population and of the capability to trace infected individuals. Finally, we explore the existence of a safe operating space. For Intermittent Lockdown we find only a small region in the model parameter space where the key indicators of the model stay within safe bounds, whereas this region is larger for the other exit strategies.

Modelling the impact of COVID-19-related control programme interruptions on progress towards the WHO 2030 target for soil-transmitted helminths (preprint)

Background On the 1st of April 2020, the World Health Organization (WHO) recommended an interruption of all neglected tropical disease control programmes, including soil-transmitted helminths (STH), in response to the COVID-19 pandemic. This paper investigates the impact of this disruption on the achieved progress towards the WHO 2030 target for STH. Methods We used two stochastic individual-based models to simulate the impact of missing one or more preventive chemotherapy (PC) rounds in different endemicity settings. We also investigate the extent to which the impact can be lessened by mitigation strategies, such as semi-annual or community-wide PC. Results Both models show that even without a mitigation strategy, control programmes will catch up by 2030. The catch-up time is limited to a maximum of 4.5 years after the interruption. Mitigations strategies may reduce this catch-up time by up to two years and can even increase the probability of achieving the 2030 target. Conclusions Though a PC interruption will only temporarily impact the progress towards the WHO 2030 target, programmes are encouraged to restart as soon as possible to minimise the impact on morbidity. The implementation of suitable mitigation strategies can turn the interruption into an opportunity to accelerate the progress toward reaching the target.

Modelling the impact of COVID-19-related programme interruptions on visceral leishmaniasis in India (preprint)

BackgroundIn March 2020, India declared a nationwide lockdown to control the spread of COVID-19. As a result, control efforts against visceral leishmaniasis (VL) were interrupted. MethodsUsing an established age-structured deterministic VL transmission model, we predicted the impact of a 6 to 24-month programme interruption on the timeline towards achieving the VL elimination target, as well as on the increase of VL cases. We also explored the potential impact of a mitigation strategy after the interruption. ResultsDelays towards the elimination target are estimated to range between 0 to 9 years. Highly endemic settings where control efforts have been ongoing for 5-8 years are most affected by an interruption, for which we identified a mitigation strategy to be most relevant. However, more importantly, all settings can expect an increase in the number of VL cases. This increase is substantial even for settings with a limited expected delay in achieving the elimination target. ConclusionBesides implementing mitigation strategies, it is of great importance to try and keep the duration of the interruption as short as possible, to prevent new individuals from becoming infected with VL, and continue the efforts towards VL elimination as a public health problem in India.

A phased lift of control: a practical strategy to achieve herd immunity against Covid-19 at the country level (preprint)

Most countries are affected by the Covid-19 pandemic and experience rapidly increasing numbers of cases and deaths. Many have implemented nationwide stringent control to avoid overburdening the health care system. This paralyzes economic and social activities until the availability of a vaccine, which may take years. We propose an alternative exit strategy to develop herd immunity in a predictable and controllable way: a phased lift of control. This means that successive parts of the country (e.g. provinces) stop stringent control, and Covid-19-related IC admissions are distributed over the country as the whole. Importantly, vulnerable individuals need to be shielded until herd immunity has developed in their area. We explore the characteristics and duration of this strategy using a novel individual-based model for geographically stratified transmission of Covid-19 in a country. The model predicts that individuals will have to experience stringent control for about 14 months on average, but this duration may be significantly shortened by future developments (more IC beds, better drugs). Clearly, the strategy will have a profound impact on individuals and society, and should therefore be considered carefully by various other disciplines (e.g. health systems, ethics, economics) before actual implementation.