Comparing lagged impacts of mobility changes and environmental factors on COVID-19 waves in rural and urban India: a Bayesian spatiotemporal modelling study.

Previous research in India has identified urbanisation, human mobility and population demographics as key variables associated with higher district level COVID-19 incidence. However, the spatiotemporal dynamics of mobility patterns in rural and urban areas in India, in conjunction with other drivers of COVID-19 transmission, have not been fully investigated. We explored travel networks within India during two pandemic waves using aggregated and anonymized weekly human movement datasets obtained from Google, and quantified changes in mobility before and during the pandemic compared with the mean baseline mobility for the 8-week time period at the beginning of 2020. We fit Bayesian spatiotemporal hierarchical models coupled with distributed lag non-linear models (DLNM) within the integrated nested Laplace approximate (INLA) package in R to examine the lag-response associations of drivers of COVID-19 transmission in urban, suburban, and rural districts in India during two pandemic waves in 2020-2021. Model results demonstrate that recovery of mobility to 99% that of pre-pandemic levels was associated with an increase in relative risk of COVID-19 transmission during the Delta wave of transmission. This increased mobility, coupled with reduced stringency in public intervention policy and the emergence of the Delta variant, were the main contributors to the high COVID-19 transmission peak in India in April 2021. During both pandemic waves in India, reduction in human mobility, higher stringency of interventions, and climate factors (temperature and precipitation) had 2-week lag-response impacts on the R t of COVID-19 transmission, with variations in drivers of COVID-19 transmission observed across urban, rural and suburban areas. With the increased likelihood of emergent novel infections and disease outbreaks under a changing global climate, providing a framework for understanding the lagged impact of spatiotemporal drivers of infection transmission will be crucial for informing interventions.

Untangling the changing impact of non-pharmaceutical interventions and vaccination on European COVID-19 trajectories.

Non-pharmaceutical interventions (NPIs) and vaccination are two fundamental approaches for mitigating the coronavirus disease 2019 (COVID-19) pandemic. However, the real-world impact of NPIs versus vaccination, or a combination of both, on COVID-19 remains uncertain. To address this, we built a Bayesian inference model to assess the changing effect of NPIs and vaccination on reducing COVID-19 transmission, based on a large-scale dataset including epidemiological parameters, virus variants, vaccines, and climate factors in Europe from August 2020 to October 2021. We found that (1) the combined effect of NPIs and vaccination resulted in a 53% (95% confidence interval: 42-62%) reduction in reproduction number by October 2021, whereas NPIs and vaccination reduced the transmission by 35% and 38%, respectively; (2) compared with vaccination, the change of NPI effect was less sensitive to emerging variants; (3) the relative effect of NPIs declined 12% from May 2021 due to a lower stringency and the introduction of vaccination strategies. Our results demonstrate that NPIs were complementary to vaccination in an effort to reduce COVID-19 transmission, and the relaxation of NPIs might depend on vaccination rates, control targets, and vaccine effectiveness concerning extant and emerging variants.

Host-seeking efficiency can explain population dynamics of the tsetse fly Glossina morsitans morsitans in response to host density decline.

Females of all blood-feeding arthropod vectors must find and feed on a host in order to produce offspring. For tsetse-vectors of the trypanosomes that cause human and animal African trypanosomiasis-the problem is more extreme, since both sexes feed solely on blood. Host location is thus essential both for survival and reproduction. Host population density should therefore be an important driver of population dynamics for haematophagous insects, and particularly for tsetse, but the role of host density is poorly understood. We investigate the issue using data on changes in numbers of tsetse (Glossina morsitans morsitans Westwood) caught during a host elimination experiment in Zimbabwe in the 1960s. During the experiment, numbers of flies caught declined by 95%. We aimed to assess whether models including starvation-dependent mortality could explain observed changes in tsetse numbers as host density declined. An ordinary differential equation model, including starvation-dependent mortality, captured the initial dynamics of the observed tsetse population. However, whereas small numbers of tsetse were caught throughout the host elimination exercise, the modelled population went extinct. Results of a spatially explicit agent-based model suggest that this discrepancy could be explained by immigration of tsetse into the experimental plot. Variation in host density, as a result of natural and anthropogenic factors, may influence tsetse population dynamics in space and time. This has implications for Trypanosoma brucei rhodesiense transmission. Increased tsetse mortality as a consequence of low host density may decrease trypanosome transmission, but hungrier flies may be more inclined to bite humans, thereby increasing the risk of transmission to humans. Our model provides a way of exploring the role of host density on tsetse population dynamics and could be incorporated into models of trypanosome transmission dynamics to better understand how spatio-temporal variation in host density impacts trypanosome prevalence in mammalian hosts.