Bias in mobility datasets drives divergence in modeled outbreak dynamics.

Background: Digital data sources such as mobile phone call detail records (CDRs) are increasingly being used to estimate population mobility fluxes and to predict the spatiotemporal dynamics of infectious disease outbreaks. Differences in mobile phone operators' geographic coverage, however, may result in biased mobility estimates.

Methods: We leverage a unique dataset consisting of CDRs from three mobile phone operators in Bangladesh and digital trace data from Meta's Data for Good program to compare mobility patterns across these sources.

Interactive SARS-CoV-2 dashboard for real-time geospatial visualisation of sewage and clinical surveillance data from Dhaka, Bangladesh: a tool for public health situational awareness.

Throughout the COVID-19 pandemic, many dashboards were created to visualise clinical case incidence. Other dashboards have displayed SARS-CoV-2 sewage data, largely from countries with formal sewage networks. However, very few dashboards from low-income and lower-middle-income countries integrated both clinical and sewage data sets.

Real-time sewage surveillance for SARS-CoV-2 in Dhaka, Bangladesh versus clinical COVID-19 surveillance: a longitudinal environmental surveillance study (December, 2019-December, 2021).

Background: Clinical surveillance for COVID-19 has typically been challenging in low-income and middle-income settings. From December, 2019, to December, 2021, we implemented environmental surveillance in a converging informal sewage network in Dhaka, Bangladesh, to investigate SARS-CoV-2 transmission across different income levels of the city compared with clinical surveillance.

Methods: All sewage lines were mapped, and sites were selected with estimated catchment populations of more than 1000 individuals.