Factors related to human-vector contact that modify the likelihood of malaria transmission during a contained Plasmodium falciparum outbreak in Praia, Cabo Verde

Abstract

Background: Determining the reproductive rate and how it varies over time and space (R_T) provides important insight to understand transmission of a given disease and inform optimal strategies for controlling or eliminating it. Estimating R_T for malaria is difficult partly due to the widespread use of interventions and immunity to disease masking incident infections. A malaria outbreak in Praia, Cabo Verde in 2017 provided a unique opportunity to estimate R_T directly, providing a proxy for the intensity of vector-human contact and measure the impact of vector control measures.

Methods: Out of 442 confirmed malaria cases reported in 2017 in Praia, 321 (73%) were geolocated and informed this analysis. R_T was calculated using the joint likelihood of transmission between two cases, based on the time (serial interval) and physical distance (spatial interval) between them. Log-linear regression was used to estimate factors associated with changes in R_T, including the impact of vector control interventions. A geostatistical model was developed to highlight areas receptive to transmission where vector control activities could be focused in future to prevent or interrupt transmission.

Results: The R_T from individual cases ranged between 0 and 11 with a median serial- and spatial-interval of 34 days [interquartile range (IQR): 17-52] and 1,347 m (IQR: 832-1,985 m), respectively. The number of households receiving indoor residual spraying (IRS) 4 weeks prior was associated with a reduction in R_T by 0.84 [95% confidence interval (CI) 0.80-0.89; p-value <0.001] in the peak-and post-epidemic compared to the pre-epidemic period.

Conclusions: Identifying the effect of reduced human-vector contact through IRS is essential to determining optimal intervention strategies that modify the likelihood of malaria transmission and can inform optimal intervention strategies to accelerate time to elimination. The distance within which two cases are plausibly linked is important for the potential scale of any reactive interventions as well as classifying infections as imported or introduced and confirming malaria elimination.

Keywords: Cabo Verde; malaria; reproductive number; transmission heterogeneity; transmission reduction.

Figure 1

Temporal trend of the estimated mean R_T per week of cases reported through 2017. Malaria cases classified as imported are shown in red with locally acquired infections in blue. The size of the points represents the number of cases reported in that week with the maximum and minimum estimated R_T highlighted by the light gray lines. Points where the range is not shown reflect only having a single case reported that week. The vertical gray lines represent the different phases of the epidemic: Pre-epidemic from January June or weeks 0–26; Peak-epidemic from July to September or weeks 27–39 and; Post-epidemic from October December or weeks 40–52.

Figure 2

Weekly trends of the epidemic and interventions implemented by the program to contain the outbreak. (A) Temporal trends in the estimated Rt per case (red points) over the course of 2017 highlighting the epidemic period. The x-axis shows the weeks starting in 2017 through the end of the pandemic with the y-axis showing the estimated Rt. (B) The number of structures sprayed with indoor residual spray (IRS) and (C) the number of people tested per week as part of reactive case detection (RACD) activities per week over the same time period. The vertical gray lines represent the different phases of the epidemic: Pre-epidemic from January June or weeks 0–26; Peak-epidemic from July September or weeks 27–39 and; Post-epidemic from October December or weeks 40–52.

Figure 3

Map of malaria receptivity in Praia, Cabo Verde based on the 2017 outbreak. (A) Map of the estimated R_T as predicted by the geostatistical model informed by environmental covariates as predictors. (B) The probability that the area has a predicted R_T > 1. The areas where there is at least 80% probability that R_T is > 1 are marked by the black hashed areas. This arbitrary threshold was selected for visualization purposes, with the specific areas of programmatic interest being dependent on the goals of the program and resources available. These areas are thus ones where malaria is particularly receptive and may spark future epidemics if parasites are imported when the Anopheles mosquito vectors are active.