Introduction

The 2014–2015 Ebola outbreak continues to have a global impact. Prevention and preparedness measures remain in place at airports and hospitals across the United States and Europe, while ongoing transmission in West Africa has led to a case count exceeding 28,450 [1]. Since October 2014, however, the epidemic has been diminishing. Despite a recent resurgence in Liberia, efforts have shifted from emergency response to endgame strategies. Fundamental to preventing or mitigating future outbreaks is the identification of factors that exacerbate risks of Ebola emergence, transmission and geographical dissemination. While it has been appreciated that insufficient healthcare infrastructure in Liberia, Guinea and Sierra Leone has been a primary obstacle in the treatment of Ebola, socioeconomic heterogeneities within these countries have not been evaluated as determinants of Ebola transmission. In Liberia, specifically, more than 1.4 million of the country’s extreme poor have incomes less than $0.50 per day [2], adult literacy rates are under 43%, and public spending allocated to health is only 4% of the GDP [3]. Approximately, 68% of its urban population resides in a network of slums [4] characterized by overcrowding, high crime, and lack of sanitation [5–7]. Even for the remainder of the population, there are stark differences regarding population density and improved sanitation between middle and higher SES.

To determine the role of poverty in the Ebola outbreak, we analyzed transmission chains using a time-dependent stochastic model that was adapted to evaluate the heterogeneity of Ebola incidence and transmission factors for over 300 communities within Liberia between February and December 2014. We found that cases from low and middle SES regions have significantly more contacts when infectious and lead to much greater transmission than cases from higher SES regions. Nonetheless, all SES regions responded to instrumental interventions aimed at rapid hospitalization, concomitantly reducing community transmission and improving case fatality over the course of the outbreak.

Methods

Model

Parameterization.

We extended a model [11] that integrates Ebola disease progression of individual infections with contact data to quantify transmission within and among community types. The model was parameterized with daily viral load and daily contact rate of each infected individual to stochastically assign secondary cases. By integrating the CCD, we generated and incorporated a contact matrix for individuals across the 22 zones of Montserrado County (S1 Fig). Contact behavior was expected to change during the epidemic [12, 13], such as due to reduced movement between zones with increased use of blockades and curfews that were in flux and being established between July to September. We therefore generated separate matrices of contacts specifically for July, August and September, the months of greatest transmission and when contact behavior is expected to shift. Since the CTD only included cases through the first two weeks of October and since most interventions that would impact inter-zone movement or interactions had been instituted by the end of September, contacts in October and November were assumed to follow the same inter-zone matrix as those in September. Extending the previous model to include zone-specific contact structure enabled us to capture transmission patterns between communities.

A total of 2,500 confirmed and probable cases with symptom onset in July through November were included in the modeling analyses. Individuals reporting a potential source case in either the CCD or CTD were assumed to have been infected by their reported sources, while all other cases in the dataset were assigned probabilistically. The CCD were used to determine their date of symptom onset, time between onset and hospitalization if applicable, survivorship status, and location (i.e., hospital or community) of funeral if applicable. To address incomplete data, we generated a monthly distribution for each model parameter based on documented data and sampled values for infected individuals with missing information. All parameters and their distributions are detailed in Table 1.

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Table 1. Distributions for model parameters.

https://doi.org/10.1371/journal.pntd.0004260.t001

Simulation study.

To determine the likely source from which each case was infected, we conducted 1000 stochastic simulations of the model. We probabilistically sampled from the empirical distributions outlined in Table 1 to determine the relative likelihood of each individual in his or her infectious period to transmit to a newly infected individual over the course of an incubation period, given date of symptom onset. Cases who reported seeking care were assumed to no longer transmit within the community beyond their reported date of hospitalization. Given a month-specific probability of traditional burial, non-survivors could become a source case post-mortem. All deceased cases with hospital funerals were assumed to have hygienic burials and thus did not contribute to post-mortem transmission. The number of secondary cases was determined for each source from 1000 iterations of the model. In each simulation, we tracked the SES of sources and secondary cases to evaluate transmission patterns within and between community types. We then calculated a risk ratio by dividing the proportion of secondary cases in each SES group given the SES level of the source by the proportion expected under a homogenous model.

Results

The case classification dataset was analyzed for the 3,532 cases meeting our inclusion criteria and with dates of symptom onset between February 28, 2014 and December 1, 2014. On average, relative to probable and confirmed cases of Ebola in high SES areas, cases in communities of middle and low SES reported seeking care less frequently, although the difference is not statistically significant (Table 2). For those who presented at an Ebola treatment unit (ETU) or other health care facility, there were no significant differences in time from symptom onset to hospitalization. A decreasing trend was observed in time to hospitalization for all three SES groups between July and November 2014 (S6 Fig).

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Table 2. Key factors of Ebola transmission based on socioeconomic status (SES) of probable and confirmed cases¹.

https://doi.org/10.1371/journal.pntd.0004260.t002

Cases in areas of low and middle SES were associated with a statistically significantly higher number of contacts. In particular, cases reporting residence in a low SES community had an average of nearly three more contacts, as compared to individuals residing in the high SES areas (10.31 versus 7.41 average contacts, p<0.001). Although the number of contacts reported by all SES groups increased over the course of the epidemic, cases from low SES communities were consistently associated with more (S6 Fig). No statistically significant differences in mortality rate were observed across the three SES levels (p = 0.240), but pairwise comparisons suggested that the case fatality for the low SES communities (46.50%) and middle SES communities (41.70%) tended to be higher than that (42.53%) for the high SES communities (p = 0.257 and p = 0.106, respectively).

Source cases reporting a low SES residence were associated with 3.5 times as many secondary cases as those sources reporting a high SES residence (95% CI: 3.1–3.9) (Table 3). In addition, the majority of index cases in the high SES areas transmitted to secondary cases in high SES areas, while only about a third of secondary cases originating from index cases in low SES areas were also in low SES areas. These observations provide evidence that cases tended to be exported from poverty areas and result in transmission chains across Montserrado County.

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Table 3. Risk ratio of transmission to secondary cases in low, middle, or high SES communities, given SES of the source case.

https://doi.org/10.1371/journal.pntd.0004260.t003

Discussion

Our analyses show that despite widespread poverty throughout Liberia, less developed, more resource-constrained areas in Montserrado County tended to have more contacts after Ebola symptom onset and lead to more widespread transmission than higher SES communities. The significantly higher number of reported contacts by sources from low SES areas is consistent with overcrowding and lack of education on routes of disease transmission and prevention. Overall, cases from middle and low SES communities were associated with 1.5 and 3.5 times as many secondary cases, respectively, as sources from high SES communities.

We found that infected individuals who were residents of low SES communities were more likely to export Ebola to other SES communities than infected individuals within higher SES communities. Thus, not only were low SES communities disease hotspots, but they were also catalysts of spread throughout Monrovia. Employment opportunities are typically outside of the lowest SES communities, leading to substantial daily movement back and forth from slums throughout Monrovia.

Our findings suggest that targeting areas of extreme poverty would have the greatest impact in terms of preventing or containing outbreaks of infectious disease. However, the construction and staffing of hospitals or other public health infrastructure in the most disadvantaged areas of Liberia is a daunting even if essential undertaking, particularly in the context of worsened socioeconomic conditions at the country level due to the recent Ebola outbreak [21]. Therefore, optimal resource allocation to prevent widespread infectious disease will require improvements beyond hospital infrastructure [22].

Poverty has been correlated with increased rates of other infectious disease. In malaria endemic settings, for instance, living in traditional or unimproved homes with mud walls, thatched roofs, and earth floors has been associated with over twice the risk of infection than living in modern homes [23]. In Liberia, more specifically, cholera and other diarrheal diseases are highly prevalent in densely populated coastal regions of Monrovia [24, 25]. Poor sanitation conditions in these urban slums, where less than 25% of households have access to improved facilities, along with a lack of trash removal, result in regular contamination of the high water table [25]. Furthermore, overcrowding provides an environment conducive to the rapid spread and challenging containment of infectious disease. Focusing on sustainable development in urban slums and other communities of low and middle SES could significantly reduce the risk of future infectious disease outbreaks.

Despite the lack of infrastructure in Liberia’s urban slums, strong social networks exist. These networks afford opportunities for grassroots efforts to effectively engage community members in combatting infectious disease. The potential of community-driven efforts to contain Ebola spread became apparent in September when culturally sensitive messaging was used with leaders in the West Point slum to facilitate active case finding. The effectiveness of this approach is evidenced by our finding of increasingly prompt hospitalization, reducing transmission within the community [26], as well as by the organized response to and control of the recent outbreak in Nedowein, Liberia. Thus although poverty substantially exacerbates Ebola transmission, the obstacles imposed by poverty are not insurmountable by a targeted approach tailored to the specific needs and challenges faced by impoverished communities.

Supporting Information