Democratic Republic of the Congo: Projected COVID-19 Sub-national Cases

The Center for Humanitarian Data established a partnership with the Johns Hopkins University Applied Physics Laboratory to develop a COVID-19 model which provides projections and insights related to the scale of the crisis, the duration of the crisis in a specific location, and how different response interventions are expected to impact the epidemic curve.

The team is using an SEIR (Susceptible, Exposed, Infectious, Recovered) model of infectious disease dynamics which is considered the simplest and most effective technique used in the literature. The model is based on a progression from susceptible to either recovered or dead. Inputs include the reproduction rate (Ro), case fatality rate (CFR), and estimated probabilities that an individual person may contract COVID-19. The model then simulates an outbreak and provides estimates for cases, hospitalizations, and deaths.

Parameters: ● Basic reproduction number: R0 (β/y) ○ Transmission rate: β ○ Infectious period: 1/y ● Case Fatality Ratio (CFR): (1-f) ○ Probability of recovery: f ● Latent period after exposure: 1/σ

Limitations: ● Multi-strain systems ● Time-varying infectivity ● Heterogeneous population ● Capturing pockets of an outbreak

The key features of the model include: ● Tuning on reported data. The estimation of the main parameters (mainly the reproduction rate R0 and the case reporting rate) is tuned according to the observed recent trends in reported COVID-19 cases. ● Subnational. The model provides COVID-19 projections at the subnational level, matching the administrative level at which COVID-19 cases are reported. ● Spatial spread. The density of roads is used to estimate the expected mobility patterns and to simulate the spread of COVID-19 between administrative units. ● Population stratification. The model fidelity is increased by taking into consideration: ○ The age structure of the population at the subnational level ○ The expected probability of contact between populations of different age groups, including contacts expected to happen at work, school, home and everywhere else (social mixing) ○ Vulnerability factors such as food insecurity, household air pollution and access to hand washing facilities. ● Non pharmaceutical interventions - NPIs. The model simulates the expected impact of NPIs at the subnational level, and also how the outbreaks is influenced by changing NPIs implemented over time. The NPIs currently implemented can be categorised in three main groups: ○ Mobility based NPIs, which would limit the spread of disease between administrative units (e.g. border closures) ○ Contact based NPIs, which reduce the probability of contact between specific groups (e.g. shielding of the elderly, closing schools) ○ R0 based NPIs, which reduce the overall reproduction rate (e.g. awareness campaigns, curfews )

Data in this dataset has been generated from a model.