Triangulating Truth and Reaching Consensus on Population Size, Prevalence, and More: Modeling Study.

Background: Population size, prevalence, and incidence are essential metrics that influence public health programming and policy. However, stakeholders are frequently tasked with setting performance targets, reporting global indicators, and designing policies based on multiple (often incongruous) estimates of these variables, and they often do so in the absence of a formal, transparent framework for reaching a consensus estimate.

Objective: This study aims to describe a model to synthesize multiple study estimates while incorporating stakeholder knowledge, introduce an R Shiny app to implement the model, and demonstrate the model and app using real data.

Modelling the impact of CD4 testing on mortality from TB and cryptococcal meningitis among patients with advanced HIV disease in nine countries.

Introduction: Despite antiretroviral therapy (ART) scale-up among people living with HIV (PLHIV), those with advanced HIV disease (AHD) (defined in adults as CD4 count <200 cells/mm or clinical stage 3 or 4), remain at high risk of death from opportunistic infections. The shift from routine baseline CD4 testing towards viral load testing in conjunction with "Test and Treat" has limited AHD identification.

Methods: We used official estimates and existing epidemiological data to project deaths from tuberculosis (TB) and cryptococcal meningitis (CM) among PLHIV-initiating ART with CD4 <200 cells/mm , in the absence of select World Health Organization recommended diagnostic or therapeutic protocols for patients with AHD.

Scale-Up of HIV Antiretroviral Therapy and Estimation of Averted Infections and HIV-Related Deaths - Uganda, 2004-2022.

On January 28, 2003, the U.S. President's Emergency Plan for AIDS Relief (PEPFAR), the largest commitment by any nation to address a single disease in history, was announced.

Adaptive network modeling of social distancing interventions.

The COVID-19 pandemic has proved to be one of the most disruptive public health emergencies in recent memory. Among non-pharmaceutical interventions, social distancing and lockdown measures are some of the most common tools employed by governments around the world to combat the disease. While mathematical models of COVID-19 are ubiquitous, few have leveraged network theory in a general way to explain the mechanics of social distancing.

A Low-Dimensional Network Model for an SIS Epidemic: Analysis of the Super Compact Pairwise Model.

Network-based models of epidemic spread have become increasingly popular in recent decades. Despite a rich foundation of such models, few low-dimensional systems for modeling SIS-type diseases have been proposed that manage to capture the complex dynamics induced by the network structure. We analyze one recently introduced model and derive important epidemiological quantities for the system.