Synthetic dataset and simulation code for 'A Mathematical Model for the Transmission of COVID-19 in Mindanao: Epidemic Prevention and Control Measures'
Description
This deposit contains the complete synthetic dataset and the simulation code used to generate all numerical results reported in the related manuscript "A Mathematical Model for the Transmission of COVID-19 in Mindanao: Epidemic Prevention and Control Measures" (submitted to the Journal of Theoretical Biology). The manuscript formulates a nine-compartment deterministic ODE model for COVID-19 transmission in Mindanao, Philippines, with compartments susceptible (S), exposed (E), symptomatic infectious (I_s), asymptomatic infectious (I_a), isolated (Q), untested symptomatic (U_s), untested asymptomatic (U_a), hospitalized (H), and recovered (R), and force of infection lambda = beta*(I_s + theta*I_a)/N. Three intervention scenarios are simulated over a 300-day horizon: Scenario 1 (baseline, no control, R0 = 2.07), Scenario 2 (relaxed measures, Re = 1.44), and Scenario 3 (strict local-government prevention and control measures, Re = 0.86). Because officially disaggregated daily case series for Mindanao were not publicly available for the study period, all parameter values are synthetic/calibrated so that the baseline scenario reproduces an unmitigated reproduction number consistent with early COVID-19 estimates. The calibration anchors are the July 26, 2020 situation reports of the Philippine Department of Health (DOH) and the World Health Organization (WHO), tabulated in data/calibration_anchors_DOH_WHO_2020-07-26.csv. Contents: - data/scenario1_baseline_timeseries.csv, scenario2_relaxed_measures_timeseries.csv, scenario3_strict_lgu_timeseries.csv: daily compartment values, days 0-300 - data/model_parameters.csv: parameter values for all scenarios (Table 1 of the manuscript) - data/initial_conditions.csv: seeding of the epidemic - data/calibration_anchors_DOH_WHO_2020-07-26.csv: DOH/WHO situation-report anchor values - code/simulate_covid19_mindanao.py: Python script that reproduces all CSVs and figures - figures/fig1_baseline.png and fig2_scenarios.png: the manuscript figures as regenerated by the code - README.md: full documentation, column definitions, and verification table Funding: Research Office of Central Mindanao University.
Files
Steps to reproduce
Requires Python 3.9 or later with NumPy, SciPy, Matplotlib and pandas. To reproduce the dataset and figures: 1. Download and extract this deposit. 2. Open a terminal in the code/ directory. 3. Run: python3 simulate_covid19_mindanao.py The script integrates the nine-compartment ODE model with scipy.integrate.solve_ivp (RK45, rtol = 1e-9, atol = 1e-12) over days 0-300 for the three intervention scenarios, writes the three time-series CSVs into data/, saves fig1_baseline.png and fig2_scenarios.png into figures/, and prints the headline results for verification: Scenario 1 peak of active infections 4,540,212 on day 87 (manuscript reports 4,541,636; agreement within 0.04% solver tolerance); Scenario 2 peak 1,434,321 on day 163; Scenario 3 monotone decline with 2,101 recovered at day 300; final attack rates 34.46% (Scenario 1), 33.62% (Scenario 2), and 0.01% (Scenario 3). Parameter note: sigma = 1/5.2, gamma_h = 1/12 and omega = 1/180 are used as exact fractions; Table 1 of the manuscript displays the rounded decimals 0.192, 0.083 and 0.0056.
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Funders
- Central Mindanao University