A mathematical, classical stratification modeling approach to disentangling the impact of weather on infectious diseases: A case study using spatio-temporally disaggregated Campylobacter surveillance data for England and Wales.

Disentangling the impact of the weather on transmission of infectious diseases is crucial for health protection, preparedness and prevention. Because weather factors are co-incidental and partly correlated, we have used geography to separate out the impact of individual weather parameters on other seasonal variables using campylobacteriosis as a case study. Campylobacter infections are found worldwide and are the most common bacterial food-borne disease in developed countries, where they exhibit consistent but country specific seasonality. We developed a novel conditional incidence method, based on classical stratification, exploiting the long term, high-resolution, linkage of approximately one-million campylobacteriosis cases over 20 years in England and Wales with local meteorological datasets from diagnostic laboratory locations. The predicted incidence of campylobacteriosis increased by 1 case per million people for every 5° (Celsius) increase in temperature within the range of 8°-15°. Limited association was observed outside that range. There were strong associations with day-length. Cases tended to increase with relative humidity in the region of 75-80%, while the associations with rainfall and wind-speed were weaker. The approach is able to examine multiple factors and model how complex trends arise, e.g. the consistent steep increase in campylobacteriosis in England and Wales in May-June and its spatial variability. This transparent and straightforward approach leads to accurate predictions without relying on regression models and/or postulating specific parameterisations. A key output of the analysis is a thoroughly phenomenological description of the incidence of the disease conditional on specific local weather factors. The study can be crucially important to infer the elusive mechanism of transmission of campylobacteriosis; for instance, by simulating the conditional incidence for a postulated mechanism and compare it with the phenomenological patterns as benchmark. The findings challenge the assumption, commonly made in statistical models, that the transformed mean rate of infection for diseases like campylobacteriosis is a mere additive and combination of the environmental variables.

Epidemiology of COVID-19 in Prisons, England, 2020.

Using laboratory data and a novel address matching methodology, we identified 734 cases of coronavirus disease in 88 prisons in England during March 16-October 12, 2020. An additional 412 cases were identified in prison staff and household members. We identified 84 prison outbreaks involving 86% of all prison-associated cases.

Penetration and impact of COVID-19 in long term care facilities in England: population surveillance study.

BACKGROUND: Long-term care facilities (LTCF) worldwide have suffered high rates of COVID-19, reflecting the vulnerability of the persons who live there and the institutional nature of care delivered. This study describes the impact of the pandemic on incidences and deaths in LTCF across England. METHODS: Laboratory-confirmed SARS-CoV-2 cases in England, notified to Public Health England from 01 Jan to 25 Dec 2020, were address-matched to an Ordnance Survey reference database to identify residential property classifications. Data were analysed to characterize cases and identify clusters. Associated deaths were defined as death within 60 days of diagnosis or certified as cause of death. RESULTS: Of 1 936 315 COVID-19 cases, 81 275 (4.2%) and 10 050 (0.52%) were identified as resident or staff in an LTCF, respectively, with 20 544 associated deaths in residents, accounting for 31.3% of all COVID-19 deaths. Cases were identified in 69.5% of all LTCFs in England, with 33.1% experiencing multiple outbreaks. Multivariable analysis showed a 67% increased odds of death in residents [adjusted odds ratio (aOR): 1.67, 95% confidence interval (CI): 1.63-1.72], compared with those not residing in LTCFs. A total of 10 321 outbreaks were identified at these facilities, of which 8.2% identified the first case as a staff member. CONCLUSIONS: Over two-thirds of LTCFs have experienced large and widespread outbreaks of COVID-19, and just under one-third of all COVID-19 deaths occurring in this setting in spite of early policies. A key implication of our findings is upsurges in community incidences seemingly leading to increased outbreaks in LTCFs; thus, identifying and shielding residents from key sources of infection are vital to reduce the number of future outbreaks.