Description of data items to be extracted on the co-infection models
| Details | Description/examples |
| Research articles | Title, hyperlink, journal, author, settings, publication year (ranging from 1980 to 2024) |
| Model description | Structure of co-infections (within-host, between-host or multiple-strain co-infections) Modelling approach (deterministic, stochastic or fuzzy logic) Type of model (ordinary, fractional order, age-structured or spatiotemporal) Host species (human, animal or both) |
| Co-infection/co-dynamics | Co-infection diseases: we will record which disease(s) the model considered (eg, rotavirus/cholera, typhoid/tuberculosis and HIV/tuberculosis/pneumonia) Modelling of co-infection infectivity (increasing, decreasing or no change) |
| Key assumptions | Mortality assumption (additive or not additive) Transition to a co-infection class (from mono-infections to co-infected: infectivity modelled as increasing, decreasing or no change) Co-infection transitions and infectivity assumptions are biologically meaningful and justifiable (yes, no or partially) |
| Data fitting | Parameter estimation: eg, incidence rates, pathogen ingestion rates, recovery rates and mortality rates (literature, simulated data, epidemiological data, or laboratory data) Data availability (yes or no). Cases without data will be denoted as ‘none.’ Articles applying multiple data sources will be denoted as ‘multi-data.’ |
| Intervention measures | Intervention strategies to manage and control infectious diseases (non-pharmaceutical (washing, sanitation, good hygiene), treatment, vaccinations, screening, isolations and quarantine, education campaigns, vector control, antimicrobial stewardship and personal protection) |
| Model evaluation | Sensitivity analyses (yes or no). If yes, we assess the nature of sensitivity (local or global) analysis and sensitivity approaches (graphical or numerical) Model validations (yes or no). If yes, we determine the number of infections validated (one or both or multiple (more than two infections)) and determine the type of datasets (simulated or surveillance data). For surveillance data, we further determine the datasets (incidence, vaccinated, hospitalised or death case counts). Studies using multiple datasets for validation will be denoted ‘multi-data.’ In addition, we state the setting in which the model was validated (city or country or state or multi-states) Findings on impact(s) of co-infections on disease severity (increasing, decreasing or no effect) |