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lopmanlab/COVID_serovax_Mozambique_v2

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Summary

This repository contains the updated code used in simulations comparing serologically-triggered versus fixed-time interval long-term COVID-19 vaccination strategies in Mozambique. Our findings are described in this preprint entitled "Can long-term COVID-19 vaccination be improved by serological surveillance?: a modeling study for Mozambique"

Code description

Main forward model simulation and analysis

Code for the main modeling work and analysis can be found in 0_model. The 10-year forward model simulations were run using a High Performance Cluster. Relevant pieces of the main code are detailed in the table below. For the serologically-triggered vaccination scenarios, vaccination was implemented using an event function in the model setup through the DeSolve package framework for solving ODEs. For fixed-time interval vaccination, the event function was removed and vaccination was implemented directly in the model code to avoid accidental triggering of vaccination. While the codes are separated, all other code structure, initial conditions and parameters were the same between the two vaccination scenarios. Model outputs of deaths, cases, seroprevalence over time, vaccine doses from each model run from the randomly sampled annual Rts were then summarized and interim results are made available in 0_res. These results can be further summarized for each vaccine scenario (seroprevalence triggeres of 50%-80% and biennial and annual fixed-time vaccinations) into medians and ranges which are used to produce tables and figures estimating vaccine impact.

Description of main model code and processing of raw model outputs

The model code and processing pipeline is used for all of the scenario analysis detailed in the subsequent sections.

File Description Category
0_model/0_model_code_sero Model code function using serology to trigger vaccinations Serology-triggered models
0_model/0_model_setup Setup model with seroprevalence vax trigger Sero-trigger models
0_model/0_model_setup_hiescape Setup model with seroprevalence vax trigger & high immune escape Sero-trigger models
0_model/0_model_setup_randtime Setup model with seroprevalence vax trigger & randomly-timed epidemics Sero-trigger models
0_model/1_model_code_int Model code function with fixed-time vaccinations Fixed-time models
0_model/1_model_setup Setup model with fixed-time trigger Fixed-time models
0_model/1_model_setup_hiescape Setup model with fixed-time vax trigger& high immune escape Fixed-time models
0_model/0_model_setup_randtime Setup model with fixed-time vax trigger & randomly-timed epidemics Fixed-time models
9_last_Rrand Distribution of compartments at end of calibration Model input
9_mixing_matrix_gmix Social mixing matrix input Model input
9_spec_humid Specific humidity over calendar year Model input
0_postprocess/0_case_sero_death_timeseries Takes raw outputs from models and summarize into time-series Compile results &summarise
0_postprocess/0_imm_timeseries Takes raw outputs from simulations and summarize into time-series (immune landscape) Compile results &summarise
0_postprocess/0_nnt_sero Takes raw outputs from simulations and summarize results for NNV Compile results &summarise

Sample folder structure for model input/output and result generation for a single scenario

File Description
0_plot Plots and figures from scenario
0_res Summarized results from scripts in 0_post_process
0_sweep_sero Data frame of model parameter inputs for serology-triggered vax scenarios
0_sweep_int Data frame of model parameter inputs for fixed-time vax scenarios
2_combine_res_vax Combine model runs and summarise with scripts in 0_postprocess[0_postprocess]
3_plots Time series plots and tables
3_plot_nnt Plots and tables for NNV
3_plots_corr Plots and tables for correlation

Sensitivity analysis

The structure of code for the sensitivity analysis repicate the same structure as the main analysis. The following sensitiivty analysis were conducted

Model calibration

The code used for model calibration implemented using Approximate Bayesian Approach can be found in 0_calibration

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