A Model for the Creation of a Predictive Healthcare Coverage Map in Yemen

Abstract IntroductionThe ongoing war in Yemen continues to pose challenges for health care workers in the country. The fighting has destroyed public infrastructure including primary and secondary health care facilities, hindered the movement of people, food, fuel, medical supplies, and information, and restricted access to and availability of social services including safe drinking water and sanitation. This has led to the increase in the spread of diarrheal diseases, including cholera, which, despite the efficacy of zinc and oral rehydration salt solutions to treat the resulting dehydration, remains one of greatest sources of mortality in children under five years old. In contexts such as Yemen, Health Management Information Systems and Surveillance Systems are weak and unreliable to begin with, with conflict and linked disruption of social services these systems are further weakened making monitoring of the situation and evidence-based planning and implementation even more difficult. Without information on the total number of children suffering from these diseases, it is difficult for health officials and aid organizations to make policy level decisions, inform annual and humanitarian response plans, set targets, mobilize resources, order supplies, deploy resources (human and supplies) and monitor based on needs, leading to poor quality decisions. These reasons, coupled with lack of access, security, and financial and human resources make it even more important in conflict settings, than in non-conflict settings, to know where it is best to invest. This manuscript looks at the development of a computational model designed to draw upon available health data and supplement it with additional sources and acceptable assumptions to provide some of the missing data via health access chart to better inform decision making on the above-mentioned policies. This chart is designed to show what percentage of the total estimated sick population is receiving medical assistance without the need for health workers to place themselves in the way of any additional harm.MethodsA Markov model, which is a probabilistic model that shows how a population moves between different states overtime, was created based on an analysis of Yemen clinical register data from the Ministry of Public Health collected through a third party hired for monitoring purposes covering the period of May through September of 2018. The model was designed with four states for children to transition between over a weekly basis. The probability that a child transitioned from the Sick state to the In-treatment state during any given week was a time varying function based on the average precipitation recorded monthly for 115 years and the state of the roads and bridges during that week as assessed by the World Food Program. The model examined the number of children treated, incidence rate, mortality rate, treatment efficacy and treatment mortality. Once validated, the model was run for 2019 to provide the weekly estimated coverage of children being treated for diarrheal diseases throughout all of Yemen. ResultsThe model was able to recreate the observed trends in treatment on the ground with no significant difference between model output and provided validation data for all metrics. When combined with infrastructure data, the curve of best fit created for the precipitation values depicted a seasonal increase in the number of estimated new diarrheal cases in children under five and a resulting decreasing in the number receiving treatment. This combination has led predictions for the percent coverage to range between an average weekly minimum of 1.73% around the 28th week of the year to a weekly maximum weekly coverage of just over 5% around the new year. ConclusionThe model created and presented in this manuscript shows a seasonal trend in the spread of diarrheal disease in children under five living in Yemen. Despite the assistance of aid organizations in attending to those in need, during the mid-year rains up to 98% are unable to receive medical aid. The coverage map indicates that community outreach or other types of assistance where aid proactively goes out to those in need should be scaled up during and just prior to these periods. This would serve to offset the decrease in the number receiving treatment by lessening the prohibitive travel burden on families during these times.