Genomic Epidemiology of 2023-2024 Oropouche Outbreak in Iquitos, Peru reveals independent origin from a concurrent outbreak in Brazil.

Oropouche virus is an arbovirus endemic to the Americas. Periodic outbreaks have occurred since its description in 1955. In late 2023, an outbreak occurred in Peru, centered in and around Iquitos in the Eastern Peruvian Amazon.

Spatial variation in housing construction material in low- and middle-income countries: A Bayesian spatial prediction model of a key infectious diseases risk factor and social determinant of health.

Housing infrastructure and quality is a major determinant of infectious disease risk and other health outcomes in regions where vector borne, waterborne and neglected tropical diseases are endemic. It is important to quantify the geographical distribution of improvements to dwelling components to identify and target resources towards populations at risk. This study aimed to model the sub-national spatial variation in housing materials using covariates with quasi-global coverage and use the resulting estimates to map predicted coverage across the world's low- and middle-income countries.

Epstein-Barr Virus in the RIVERA Case-Control Study of Acute Febrile Illness: Acute Mononucleosis Nearly Absent as an Etiology in the Peruvian Amazon.

Large diagnostic panels allow for pathogens with high or low likelihood of causing attributable illness to be tested simultaneously. Infectious mononucleosis (IM) due to primary infection with Epstein-Barr virus (EBV) is a common cause of acute febrile illness (AFI) in case series from high-income countries, though its contribution to AFI in tropical low-income settings is unclear. As part of a case-control study using multiplex quantitative polymerase chain reaction (qPCR) diagnostics, we set out to determine if primary EBV infection was an underrecognized cause of AFI in the Peruvian Amazon.

Assessment and comparison of model estimated and directly observed weather data for prediction of diarrhoea aetiology.

Recent advances in clinical prediction for diarrhoeal aetiology in low- and middle-income countries have revealed that the addition of weather data to clinical data improves predictive performance. However, the optimal source of weather data remains unclear. We aim to compare the use of model estimated satellite- and ground-based observational data with weather station directly observed data for the prediction of aetiology of diarrhoea.