title: Quantifying Poverty as a Driver of Ebola Transmission.
abstract: BACKGROUND
  Poverty has been implicated as a challenge in the control of the current Ebola outbreak in West Africa. Although disparities between affected countries have been appreciated, disparities within West African countries have not been investigated as drivers of Ebola transmission. To quantify the role that poverty plays in the transmission of Ebola, we analyzed heterogeneity of Ebola incidence and transmission factors among over 300 communities, categorized by socioeconomic status (SES), within Montserrado County, Liberia.
  
  
  RESULTS
  We evaluated 4,437 Ebola cases reported between February 28, 2014 and December 1, 2014 for Montserrado County to determine SES-stratified temporal trends and drivers of Ebola transmission. A dataset including dates of symptom onset, hospitalization, and death, and specified community of residence was used to stratify cases into high, middle and low SES. Additionally, information about 9,129 contacts was provided for a subset of 1,585 traced individuals. To evaluate transmission within and across socioeconomic subpopulations, as well as over the trajectory of the outbreak, we analyzed these data with a time-dependent stochastic model. Cases in the most impoverished communities reported three more contacts on average than cases in high SES communities (p<0.001). Our transmission model shows that infected individuals from middle and low SES communities were associated with 1.5 (95% CI: 1.4-1.6) and 3.5 (95% CI: 3.1-3.9) times as many secondary cases as those from high SES communities, respectively. Furthermore, most of the spread of Ebola across Montserrado County originated from areas of lower SES.
  
  
  CONCLUSIONS
  Individuals from areas of poverty were associated with high rates of transmission and spread of Ebola to other regions. Thus, Ebola could most effectively be prevented or contained if disease interventions were targeted to areas of extreme poverty and funding was dedicated to development projects that meet basic needs.
authors.affiliation: Community-Based Initiative, Ministry of Health, Monrovia, Liberia.|National Institute of Allergy and Infectious Diseases, PREVAIL-III Study, Monrovia, Liberia.|A.M. Dogliotti College of Medicine, University of Liberia, Monrovia, Liberia.|Center for Infectious Disease Modeling and Analysis, Yale School of Public Health, New Haven, Connecticut, United States of America.
authors.forename: Mosoka P
authors.initials: MP
authors.lastname: Fallah
authors.affiliation: Center for Infectious Disease Modeling and Analysis, Yale School of Public Health, New Haven, Connecticut, United States of America.
authors.forename: Laura A
authors.initials: LA
authors.lastname: Skrip
authors.affiliation: Center for Infectious Disease Modeling and Analysis, Yale School of Public Health, New Haven, Connecticut, United States of America.
authors.forename: Shai
authors.initials: S
authors.lastname: Gertler
authors.affiliation: Center for Infectious Disease Modeling and Analysis, Yale School of Public Health, New Haven, Connecticut, United States of America.|Department of Industrial Engineering, Tel Aviv University, Tel Aviv, Israel.
authors.forename: Dan
authors.initials: D
authors.lastname: Yamin
authors.affiliation: A.M. Dogliotti College of Medicine, University of Liberia, Monrovia, Liberia.|Center for Infectious Disease Modeling and Analysis, Yale School of Public Health, New Haven, Connecticut, United States of America.
authors.forename: Alison P
authors.initials: AP
authors.lastname: Galvani
mesh_terms: D004196:Disease Outbreaks / Q000191:economics / Q000517:prevention & control*; D005260:Female; D019142:Hemorrhagic Fever, Ebola / Q000191:economics / Q000517:prevention & control / Q000635:transmission*; D006801:Humans; D015994:Incidence; D007988:Liberia / Q000453:epidemiology; D008297:Male; D018803:Models, Economic; D011203:Poverty / Q000706:statistics & numerical data*; D012111:Residence Characteristics; D012923:Social Class; D013269:Stochastic Processes