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In the context of polio eradication efforts, accurate assessment of vaccination programme effectiveness is essential to public health planning and decision making. Such assessments are often based on zero-dose children, estimated using the number of children who did not receive the first dose of the Diphtheria-Tetanus-Pertussis containing vaccine as a proxy. Our study introduces a novel approach to directly estimate the number of children susceptible to poliovirus type 2 (PV2) and uses this approach to provide district-level estimates for South Africa of susceptible children born between 2017 and 2022. We used district-level data on annual doses of inactivated poliovirus vaccine (IPV) administered, live births, and population sizes, from 2017 through 2022. We imputed missing vaccination data, implemented flexible assumptions regarding dose distribution in the eligible population, and used estimated efficacy values for one, two, three, and four doses of IPV, to compute the number of susceptible and immune children by birth year. We validated our approach by comparing an intermediary output with zero-dose children (ZDC) estimated using data reported by WHO/UNICEF Estimates of National Immunization Coverage (WUENIC). Our results indicate high heterogeneity in susceptibility to PV2 across South Africa's 52 districts as of the end of 2022. In children under 5 years, PV2 susceptibility ranged from approximately 30 % in districts including Xhariep (31.9 %), Ekurhuleni (30.1 %), and Central Karoo (29.8 %), to less than 4 % in Sarah Baartman (1.9 %), Buffalo City (2.1 %), and eThekwini (3.2 %). Our susceptibility estimates were consistently higher than ZDC over the timeframe. We estimated that ZDC decreased nationally from 155,168 (152,737-158,523) in 2017 to 108,593 in 2021, and increased to 127,102 in 2022, a trend consistent with ZDC derived from data reported by WUENIC. While our approach provides a more comprehensive profile of PV2 susceptibility, our susceptibility and ZDC estimates generally agree in the ranking of districts according to risk., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

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.