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Predicting the Spread of Cholera in Mozambique as Case Count Rises

Modeling aims to target vaccination campaigns and protect more people against the disease.

News

Epidemiologists at Harvard School of Public Health, with whom Direct Relief, Nethope, Facebook's Data for Good team, and the Northwestern University School of Medicine have been collaborating since the aftermath of Cyclone Idai, have updated their model to understand how the emergence of cholera cases could spread to better target vaccination efforts.
By Andrew Schroeder

In the aftermath of Cyclone Kenneth, the second major storm in only a few weeks to strike the southern African country of Mozambique, has brought cholera in its wake. Intense rains, lasting for several days, disrupted transportation, displaced families, and damaged infrastructure, including health care and sanitation, which are vital to preventing and controlling cholera.

According to the most recent reports from the Ministry of Health and the World Health Organization, three districts in the province of Cabo Delgado (Pemba, Mecufi and Metuge) have seen outbreaks of cholera since Cyclone Kenneth made landfall.

The coastal city of Pemba is the center of these outbreaks, with 108 total cases, followed by Metuge with 19, and Mecufi with 10.New cases are being detected regularly, with the most recent reporting, on May 9, recording one of the highest total case loads since the storm.

In addition to patient treatment with antibiotics and rehydration, Mozambique’s second mass cholera vaccination campaign of the year is scheduled for the end of this week.According to the most recent epidemiological bulletin, “Over 516,000 doses of the Oral Cholera Vaccine (OCV) were received by the authorities and health partners in Pemba on 12 May, for the vaccination campaign scheduled to start on 16 May.” The previous cholera vaccination campaign in Beira saw over 900,000 individuals vaccinated across three districts in the path of Cyclone Idai.

The 516,000 doses, administered in single-dose format to maximize the reach of the current supply, covers a considerable swath of the population in Cabo Delgado. However, health authorities remain vigilant to any potential changes in the geography of case detections, which might alter calculations of the total at-risk population and the scope and focus areas for vaccines.

Epidemiologists at Harvard School of Public Health, with whom Direct Relief, Nethope, Facebook’s Data for Good team, and the Northwestern University School of Medicine have been collaborating since the aftermath of Cyclone Idai, have updated their model to understand how the emergence of cholera cases may spread using a very simple gravity model, in order to make it better suited to possible tactical planning efforts for cholera vaccination. More detail about the model and its methodology can be found here.

Case counts for new cholera detections can be input variably. The model combines cases with a gravity model (simulated population movement based on population estimates from before the flood), flood extent, previous cholera incidence and El Nino sensitivity measures, to rank surrounding areas with respect to risk of cholera emerging there. As new case totals are updated, the probability of future outbreak areas may change in significant ways, alerting health authorities to the need for revised planning estimates. The model uses district boundaries for population. Therefore, new case detections recorded at the sub-district level need to be combined into district-level totals for estimation.

Members of the health cluster, including the World Health Organization and the Mozambique Ministry of Health, have been briefed on the details and functionality of this model. Documentation is in the process of being translated to Portuguese to ensure that local actors are fully informed. As the campaign is rolled out over the coming weeks, and new information becomes available, additional modeling efforts can be adapted and revised to reflect improved understanding of the outbreaks.

Click here to explore the model.

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