Alvaro Schwalb

@aschwalbc.bsky.social

2727 Followers 389 Following

Infectious disease epidemiologist | TB mathematical modeller | Research Fellow @LSTHM | Associate Researcher @IMTAvH 🇵🇪 in 🇬🇧 | Tell your dog I said hi

Poster for the Advanced TB Diagnostics course at McGill University
Table showing the number of individuals globally and by WHO region infected with viable Mycobacterium tuberculosis in 2022
Global map showing prevalence of recent viable Mycobacterium tuberculosis infection in 2022.
Course notebook for Introduction to Causal Inference course
Title page of PhD thesis on "Estimating the burden of Mycobacterium tuberculosis infection and the impact of population-wide screening for tuberculosis"
Epidemiological performance and economic impact of population-wide screening interventions in Viet Nam per algorithm when conducted to reach TB prevalence threshold of 50 per 100,000 people. The values represent cumulative accrual over the 25-year time horizon, extending up to 2050. Budget impact includes cumulative costs of screening/diagnosis and treatment for both BAU and the intervention algorithm up to 2050. The cost of front-loading refers to intervention-specific screening and treatment costs, presented as the annual average during the intervention period. Annual cost savings are defined as the difference in BAU-specific diagnosis and treatment costs between the intervention algorithm and the BAU counterfactual, averaged over the time horizon. BAU: Business-as-usual; CXR: Chest radiography; DALY: Disability-adjusted life year; FP: False positive; ICER: Incremental cost-effectiveness ratio; NAAT: Nucleic acid amplification test (Xpert MTB/RIF Ultra); TB: Tuberculosis; TP: True positive; UI: Uncertainty interval; US$: United States dollar.
Incremental costs compared to the business-as-usual (BAU) counterfactual of population-wide screening interventions in Viet Nam using a CXR+NAAT algorithm to achieve a TB prevalence of 50 per 100,000 people. Incremental costs are shown by category, with ACF representing intervention-specific costs and BAU reflecting business-as-usual TB prevention and care costs. The main analysis assumed an Xpert MTB/RIF Ultra cartridge cost of 8 USD, while a sensitivity analysis explored a reduced cartridge cost of 1 USD. Lines represent incremental costs, and the shaded areas indicate the lower (2.5% quantile) and upper (97.5% quantile) bounds.
Epidemiological performance and economic impact of population-wide screening interventions in Viet Nam per algorithm when conducted to reach TB prevalence threshold of 50 per 100,000 people. The values represent cumulative accrual over the 25-year time horizon, extending up to 2050. Budget impact includes cumulative costs of screening/diagnosis and treatment for both BAU and the intervention algorithm up to 2050. The cost of front-loading refers to intervention-specific screening and treatment costs, presented as the annual average during the intervention period. Annual cost savings are defined as the difference in BAU-specific diagnosis and treatment costs between the intervention algorithm and the BAU counterfactual, averaged over the time horizon. BAU: Business-as-usual; CXR: Chest radiography; DALY: Disability-adjusted life year; FP: False positive; ICER: Incremental cost-effectiveness ratio; NAAT: Nucleic acid amplification test (Xpert MTB/RIF Ultra); TB: Tuberculosis; TP: True positive; UI: Uncertainty interval; US$: United States dollar.
TB prevalence by each population-wide screening algorithm per threshold. Main analysis evaluates performance to reach TB prevalence of 50 per 100,000 people. Lines represent TB prevalence, and the shaded area shows the lower (2.5% quantile) and upper (97.5% quantile) bounds. Dashed lines represent TB prevalence thresholds (±10%). NAAT: Nucleic acid amplification test (Xpert MTB/RIF Ultra); CXR: Chest radiography with CAD software interpretation.
A closed population, compartmental model of tuberculosis natural history, adapting features of previously published models. The model is depicted using nine compartments allowing for Mycobacterium tuberculosis infection through TB disease. Shaded compartments indicate those that contribute to transmission. The force of infection (depicted with λ) depends upon the contact parameter and the prevalence of infectious disease (i.e., asymptomatic and symptomatic TB), accounting for the relative infectiousness of asymptomatic TB.
Participants of the “Introduction to TB modelling course”
Email from a predatory journal where the special issue guest editor is named Ricky Martin