The Flexible Farrington Algorithm (FFA) is widely used to detect infectious disease outbreaks at national/regional levels on a weekly basis. The rapid spread of SARS-CoV-2 alongside the speed at which diagnostic and public health interventions were introduced made the FFA of limited use. We describe how the methodology was adapted to provide a daily alert system to support local health protection teams (HPTs) working in the 316 English lower-tier local authorities. To minimize the impact of a rapidly changing epidemiological situation, the FFA was altered to use 8 weeks of data. The adapted algorithm was based on reported positive counts using total tests as an offset. Performance was assessed using the root mean square error (RMSE) over a period. Graphical reports were sent to local teams enabling targeted public health action. From 1 July 2020, results were routinely reported. Adaptions accommodated the impact on reporting because of changes in diagnostic strategy (introduction of lateral flow devices). RMSE values were relatively small compared to observed counts, increased during periods of increased reporting, and were relatively higher in the northern and western areas of the country. The exceedance reports were well received. This presentation should be considered as a successful proof-of-concept.

Malaria remains a major health challenge in developing countries, with climate change intensifying its impact. Pakistan is among the most vulnerable nations. This study examines the relationship between temperature and malaria cases in two highly affected districts, Bannu and Lakki Marwat, to inform climate-adaptive interventions.

We analyzed monthly malaria cases (2014–2022) from the Integrated Vector Control/Malaria Control Program in Khyber Pakhtunkhwa, combined with gridded meteorological data from Copernicus ERA5-Land. Time-series analysis using distributed lag nonlinear models and quasi-Poisson regression was applied to assess the associations.

The findings suggest that as temperatures exceed 22.4°C, malaria transmission increases by 9 to 10% for every 1°C rise in both districts. In Bannu, up to 39.8% of reported malaria cases could be attributed to heat, while in Lakki Marwat, 54.1% of cases were attributable to heat. Under high emission scenarios, heat-related malaria cases could increase by 0.8 to 3.5% by the 2060s. Relationship between temperature and malaria transmission is complex and is influenced by environmental factors such as precipitation and humidity.

Given Pakistan’s limited healthcare infrastructure, addressing climate-driven malaria risks is urgent. Recent severe floods and malaria surges highlight the need for climate adaptation measures and strengthened healthcare systems to enhance community resilience.

Whole-genome sequencing (WGS) information has played a crucial role in the SARS-CoV-2 (COVID-19) pandemic by providing evidence about variants to inform public health policy. The purpose of this study was to assess the representativeness of sequenced cases compared with all COVID-19 cases in England, between March 2020 and August 2021, by demographic and socio-economic characteristics, to evaluate the representativeness and utility of these data in epidemiological analyses. To achieve this, polymerase chain reaction (PCR)-confirmed COVID-19 cases were extracted from the national laboratory system and linked with WGS data. During the study period, over 10% of COVID-19 cases in England had WGS data available for epidemiological analysis. With sequencing capacity increasing throughout the period, sequencing representativeness compared to all reported COVID-19 cases increased over time, allowing for valuable epidemiological analyses using demographic and socio-economic characteristics, particularly during periods with emerging novel SARS-CoV-2 variants. This study demonstrates the comprehensiveness of England’s sequencing throughout the COVID-19 pandemic, rapidly detecting variants of concern, and enabling representative epidemiological analyses to inform policy.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant (B.1.1.529) rapidly replaced Delta (B.1.617.2) to become dominant in England. Our study assessed differences in transmission between Omicron and Delta using two independent data sources and methods. Omicron and Delta cases were identified through genomic sequencing, genotyping and S-gene target failure in England from 5–11 December 2021. Secondary attack rates for named contacts were calculated in household and non-household settings using contact tracing data, while household clustering was identified using national surveillance data. Logistic regression models were applied to control for factors associated with transmission for both methods. For contact tracing data, higher secondary attack rates for Omicron vs. Delta were identified in households (15.0% vs. 10.8%) and non-households (8.2% vs. 3.7%). For both variants, in household settings, onward transmission was reduced from cases and named contacts who had three doses of vaccine compared to two, but this effect was less pronounced for Omicron (adjusted risk ratio, aRR 0.78 and 0.88) than Delta (aRR 0.62 and 0.68). In non-household settings, a similar reduction was observed only in contacts who had three doses vs. two doses for both Delta (aRR 0.51) and Omicron (aRR 0.76). For national surveillance data, the risk of household clustering, was increased 3.5-fold for Omicron compared to Delta (aRR 3.54 (3.29–3.81)). Our study identified increased risk of onward transmission of Omicron, consistent with its successful global displacement of Delta. We identified a reduced effectiveness of vaccination in lowering risk of transmission, a likely contributor for the rapid propagation of Omicron.