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Exploring outbreak response vaccination for measles in the DRC

Overview

A safe and effective childhood vaccine for measles became available in 1963. In spite of its widespread distribution in the developed world, however, measles vaccine remains largely unattainable for many communities in sub-Saharan Africa, and preventable measles-related child mortality is still widespread.

A measles epidemic has been raging since the beginning of 2015 in the former province of Katanga in the southeast of Democratic Republic of Congo (DRC). According to the Ministry of Public Health, as of 20 November 2015, a total of 39,619 cases and 474 deaths have been officially reported in Katanga since early 2015. More than 77 percent of cases and 88 percent of mortalities were from children aged 1 to 5 (MSF 2016).

• This group will use a time series of measles case counts across the Katanga region of the DRC from Médecins Sans Frontières (MSF), along with associated GIS data, to address the following research aims:
  • Develop a Time-series Susceptible-Infected-Recovered (TSIR) model for measles, after Bjornstad et al. 2002, and quantify the time-varying transmission rate for measles in the Katanga region.
  • Model the impact of proposed vaccination interventions in the region, specifically extended controlled temperature condition (ECTC) outbreak response vaccination on the annual number of measles cases.
  • Conduct a cost-benefit analysis in terms of potential cases averted and number of children vaccinated when using ECTC in outbreak response vaccination.

After the above aims are successfully met, there is the potential to conduct additional analyses if group participants have time and sufficient interest. For example, gravity models could be used to form a metric of connectivity for sub-localities within the Katanga region and linked with age data for geo-referenced measles cases to explore whether inter-annual stochastic fadeout of measles is likely in the region. Additionally, alternative interventions in the routine vaccination program could be addressed.

Things to consider

• Please note that we are in the process of establishing a data sharing agreement with MSF for this project. The project is contingent on receipt of the data in time for the work to begin during the second week of the Clinic.

• This group is recommended for:
  • Participants who are interested in perfectly-immunizing childhood infections
  • Participants who are interested in vaccination strategy and public-health policy.
  • Participants who are interested in interpreting and analyzing publishable data
• This group will have the opportunity to engage in any of the following:
  • Clean and analyze an MSF-provided dataset on infected cases from the Katanga region of DRC
  • Use a Time series- Susceptible-Infected-Recovered (TSIR) framework to develop a mechanistic model to fit to the available data and explore the seasonality of this disease in DRC
  • Simulate various proposed vaccination interventions within the confines of the TSIR model
  • (If desired) Experiment with gravity models, spatial data, and the signature of stochastic fadeout for measles in this region.
• Group members should be sure to prioritize the following MMED sessions:
  • Computer Session: Introduction to model implementation
  • Computer Session: Harare data in groups
  • Lecture: Introduction to Likelihood
  • Computer Session: Lab 5: Introduction to Likelihood Lab
  • Lecture: Likelihood fitting and dynamic models, Part 1: Dynamic Model Fitting and Inference Robustness
  • Lecture: Likelihood fitting and dynamic models II

Background

Accurate modeling projections of future interventions, such as vaccination, rely on a precise characterization of epidemics trends in the past. For this reason, we seek to describe the basic epidemiology of measles in Katanga before modeling any proposed interventions through vaccination. The TSIR model has been used to accurately capture the trajectory of measles incidence in the past, both for developed world nations, where the time-varying transmission rate tends to peak during childhood school terms, and in resource-poor regions of sub-Saharan Africa, where agricultural and environmental forces have been demonstrated to underpin seasonal epidemic spikes (e.g. Ferrari et al. 2008).

Data

• MSF data on 2011-2015 measles cases in Katanga, DRC

Resources

References

• Bjornstad, ON, BF Finkenstadt, & BT Grenfell (2002) Dynamics of measles epidemics: estimating scaling of transmission rates, using a Time-series SIR model. Ecological Monographs 72(2): 169–184.
• Ferrari, MJ, RF Grais, N Bharti, AJK Conlan, ON Bjørnstad, LJ Wolfson, PJ Guerin, A Djibo & BT Grenfell (2008) The dynamics of measles in sub-Saharan Africa. Nature 450: 679-684. doi:10.1038/nature06509
• Metcalf, CJE, ON Bjørnstad, BT Grenfell, & V Andreasen (2009) Seasonality and comparative dynamics of six childhood infections in pre-vaccination Copenhagen. Proceedings of the Royal Society, Series B 276(1676): 4111–4118. doi: 10.1098/rspb.2009.1058

Tutorials

• Computer Session: Introduction to model implementation
• Computer Session: Harare data in groups
• Computer Session: Lab 5: Introduction to Likelihood Lab