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.

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.

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.

Temporal and spatial analysis of the 2014-2015 Ebola virus outbreak in West Africa

West Africa is currently witnessing the most extensive Ebola virus (EBOV) outbreak so far recorded. Until now, there have been 27,013 reported cases and 11,134 deaths. The origin of the virus is thought to have been a zoonotic transmission from a bat to a two-year-old boy in December 2013 (ref. 2). From this index case the virus was spread by human-to-human contact throughout Guinea, Sierra Leone and Liberia. However, the origin of the particular virus in each country and time of transmission is not known and currently relies on epidemiological analysis, which may be unreliable owing to the difficulties of obtaining patient information. Here we trace the genetic evolution of EBOV in the current outbreak that has resulted in multiple lineages. Deep sequencing of 179 patient samples processed by the European Mobile Laboratory, the first diagnostics unit to be deployed to the epicentre of the outbreak in Guinea, reveals an epidemiological and evolutionary history of the epidemic from March 2014 to January 2015. Analysis of EBOV genome evolution has also benefited from a similar sequencing effort of patient samples from Sierra Leone. Our results confirm that the EBOV from Guinea moved into Sierra Leone, most likely in April or early May. The viruses of the Guinea/Sierra Leone lineage mixed around June/July 2014. Viral sequences covering August, September and October 2014 indicate that this lineage evolved independently within Guinea. These data can be used in conjunction with epidemiological information to test retrospectively the effectiveness of control measures, and provides an unprecedented window into the evolution of an ongoing viral haemorrhagic fever outbreak.