What does the COVID-19 pandemic mean for the next decade of onchocerciasis control and elimination?

BackgroundMass drug administration (MDA) of ivermectin for onchocerciasis has been disrupted by the SARS-CoV-2 (COVID-19) pandemic. Mathematical modelling can help predict how missed/delayed MDA will affect short-term epidemiological trends and elimination prospects by 2030. MethodsTwo onchocerciasis transmission models (EPIONCHO-IBM and ONCHOSIM) are used to simulate microfilarial prevalence trends, elimination probabilities, and age-profiles of Onchocerca volvulus microfilarial prevalence and intensity, for different treatment histories and transmission settings, assuming no interruption, a 1-year (2020) or 2-year (2020-2021) interruption. Biannual MDA or increased coverage upon MDA resumption are investigated as remedial strategies. ResultsProgrammes with shorter MDA histories and settings with high pre-intervention endemicity will be the most affected. Biannual MDA is more effective than increasing coverage for mitigating COVID-19s impact on MDA. Programmes which had already switched to biannual MDA should be minimally affected. In high transmission settings with short treatment history, a 2-year interruption could lead to increased microfilarial load in children (EPIONCHO-IBM) or adults (ONCHOSIM). ConclusionsProgrammes with shorter (annual MDA) treatment histories should be prioritised for remedial biannual MDA. Increases in microfilarial load could have short- and long-term morbidity and mortality repercussions. These results can guide decision-making to mitigate the impact of COVID-19 on onchocerciasis elimination.

State-level tracking of COVID-19 in the United States

As of 1st June 2020, the US Centers for Disease Control and Prevention reported 104,232 confirmed or probable COVID-19-related deaths in the US. This was more than twice the number of deaths reported in the next most severely impacted country. We jointly modelled the US epidemic at the state-level, using publicly available death data within a Bayesian hierarchical semi-mechanistic framework. For each state, we estimate the number of individuals that have been infected, the number of individuals that are currently infectious and the time-varying reproduction number (the average number of secondary infections caused by an infected person). We used changes in mobility to capture the impact that non-pharmaceutical interventions and other behaviour changes have on the rate of transmission of SARS-CoV-2. Nationally, we estimated 3.7% [3.4%-4.0%] of the population had been infected by 1st June 2020, with wide variation between states, and approximately 0.01% of the population was infectious. We also demonstrated that good model forecasts of deaths for the next 3 weeks with low error and good coverage of our credible intervals.