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. 2020 Feb 11;19(1):3.
doi: 10.1186/s12942-020-0196-6.

A network analysis framework to improve the delivery of mosquito abatement services in Machala, Ecuador

Affiliations

A network analysis framework to improve the delivery of mosquito abatement services in Machala, Ecuador

Catherine A Lippi et al. Int J Health Geogr. .

Abstract

Background: Vector-borne disease places a high health and economic burden in the American tropics. Comprehensive vector control programs remain the primary method of containing local outbreaks. With limited resources, many vector control operations struggle to serve all affected communities within their districts. In the coastal city of Machala, Ecuador, vector control services, such as application of larvicides and truck-mounted fogging, are delivered through two deployment facilities managed by the Ecuadorian Ministry of Health. Public health professionals in Machala face several logistical issues when delivering mosquito abatement services, namely applying limited resources in ways that will most effectively suppress vectors of malaria, dengue, and encephalitis viruses.

Methods: Using a transportation network analysis framework, we built models of service areas and optimized delivery routes based on distance costs associated with accessing neighborhoods throughout the city. Optimized routes were used to estimate the relative cost of accessing neighborhoods for mosquito control services in Machala, creating a visual tool to guide decision makers and maximize mosquito control program efficiency. Location-allocation analyses were performed to evaluate efficiency gains of moving service deployment to other available locations with respect to distance to service hub, neighborhood population, dengue incidence, and housing condition.

Results: Using this framework, we identified different locations for targeting mosquito control efforts, dependent upon management goals and specified risk factors of interest, including human population, housing condition, and reported dengue incidence. Our models indicate that neighborhoods on the periphery of Machala with the poorest housing conditions are the most costly to access. Optimal locations of facilities for deployment of control services change depending on pre-determined management priorities, increasing the population served via inexpensive routes up to 34.9%, and reducing overall cost of accessing neighborhoods up to 12.7%.

Conclusions: Our transportation network models indicate that current locations of mosquito control facilities in Machala are not ideal for minimizing driving distances or maximizing populations served. Services may be optimized by moving vector control operations to other existing public health facilities in Machala. This work represents a first step in creating a spatial tool for planning and critically evaluating the systematic delivery of mosquito control services in Machala and elsewhere.

Keywords: Ecuador; GIS; Network analysis; Service delivery; Vector control.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
The South American country of Ecuador a contends with endemic dengue transmission, particularly in southern coastal El Oro province (b, shown in yellow). Machala (b, red star) is a port city in El Oro and the fourth largest city in the country. The Ecuadorian Ministry of Health deploys mosquito control activities in Machala through two centrally located deployment hubs (c). Mosquito abatement services formerly operated from several medical subcenters (red crosses) throughout the city. This figure was produced in ArcMap 10.4 (ESRI, Redlands, CA) using shapefiles from the GADM database of Global Administrative Areas, ver. 2.8 (gadm.org), transportation network and census data from INEC, and georeferenced facility locations provided by the MoH
Fig. 2
Fig. 2
Census variables (INEC 2010) aggregated to the census-block level in Machala, Ecuador including a population and b housing condition index (HCI). The Ecuadorian Ministry of Health provided data on (c) dengue incidence in Machala for the year 2010. This figure was produced in ArcMap 10.4 (ESRI, Redlands, CA)
Fig. 3
Fig. 3
Service areas based on driving distance from the Ecuadorian Ministry of Health’s two centrally located spray deployment hubs in Machala, Ecuador. Each polygon represents the service catchment area associated with the corresponding driving distance along Machala’s road network. This figure was produced with modeled service area output in ArcMap 10.4 (ESRI, Redlands, CA)
Fig. 4
Fig. 4
Optimized routes from closest spray hub in Machala based on driving distance, where the centroids of census blocks were specified as service destinations. This figure was produced with modeled route optimization output in ArcMap 10.4 (ESRI, Redlands, CA)
Fig. 5
Fig. 5
Estimated cost of service access for optimized driving routes from the closest mosquito spraying facility to neighborhood block centroids in Machala. Relative cost of access was determined via fuel consumption along routes and the number of trips required by mosquito control operators to treat each household in a neighborhood once, providing a visual means of comparing cost of access to demand for service. This figure was produced in ArcMap 10.4 (ESRI, Redlands, CA)
Fig. 6
Fig. 6
Location-allocation analysis results for Machala, where the best combination of facilities is chosen to minimize driving distance along the road network, prioritizing neighborhoods by distance to service hub (a), neighborhood population (b), dengue incidence (c), and housing condition (d). In each instance, one of the currently used locations is retained, while the second location for optimal delivery of mosquito abatement services depends on specified management priorities. This figure was produced with modeled optimized service locations in ArcMap 10.4 (ESRI, Redlands, CA)
Fig. 7
Fig. 7
Estimated cost of service access for routes optimized under different candidate deployment locations in Machala, prioritizing distance, neighborhood population, or dengue incidence (a), or housing condition (b), as determined via location-allocation analyses. Relative cost of access was determined via fuel consumption along routes and the number of trips required by mosquito control operators to treat each household in a neighborhood once. This figure was produced in ArcMap 10.4 (ESRI, Redlands, CA)

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References

    1. Vasconcelos PFC, Rosa APAT, Pinheiro FP, Rodrigues SG, Rosa EST, Cruz ACR, et al. Aedes aegypti, dengue, and re-urbanization of yellow fever and other South American countries—past and present situation and future perspectives. World Health Organ Dengue Bull. 1999;23:55–66.
    1. Almeida AS, Medronho RD, Valencia LI. Spatial analysis of dengue and the socioeconomic context of the city of Rio de Janeiro (Southeastern Brazil) Rev Saúde Pública. 2009;43:666–673. doi: 10.1590/S0034-89102009000400013. - DOI - PubMed
    1. Alava A, Mosquera C, Vargas W, Real J. Dengue en el Ecuador 1989–2002. Rev Ecuat Hig Med Trop. 2005;42:11–34.
    1. Aviles G. Dengue reemergence in Argentina. Emerg Infect Dis. 1999;5:575–578. doi: 10.3201/eid0504.990424. - DOI - PMC - PubMed
    1. White MT, Conteh L, Cibulskis R, Ghani AC. Costs and cost-effectiveness of malaria control interventions—a systematic review. Malar J. 2011;10:337. doi: 10.1186/1475-2875-10-337. - DOI - PMC - PubMed

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