Travel patterns and demographic characteristics of malaria cases in Swaziland, 2010–2014

Abstract

BackgroundAs Swaziland progresses towards national malaria elimination, the importation of parasites into receptive areas becomes increasingly important. Imported infections have the potential to instigate local transmission and sustain local parasite reservoirs.MethodsTravel histories from Swaziland’s routine surveillance data from January 2010 to June 2014 were extracted and analysed. The travel patterns and demographics of rapid diagnostic test (RDT)-confirmed positive cases identified through passive and reactive case detection (RACD) were analysed and compared to those found to be negative through RACD.ResultsOf 1517 confirmed cases identified through passive surveillance, 67% reported travel history. A large proportion of positive cases reported domestic or international travel history (65%) compared to negative cases (10%). The primary risk factor for malaria infection in Swaziland was shown to be travel, more specifically international travel to Mozambique by 25- to 44-year old males, who spent on average 28 nights away. Maputo City, Inhambane and Gaza districts were the most likely travel destinations in Mozambique, and 96% of RDT-positive international travellers were either Swazi (52%) or Mozambican (44%) nationals, with Swazis being more likely to test negative. All international travellers were unlikely to have a bed net at home or use protection of any type while travelling. Additionally, paths of transmission, important border crossings and means of transport were identified.ConclusionResults from this analysis can be used to direct national and well as cross-border targeting of interventions, over space, time and by sub-population. The results also highlight that collaboration between neighbouring countries is needed to tackle the importation of malaria at the regional level.Electronic supplementary materialThe online version of this article (doi:10.1186/s12936-017-2004-8) contains supplementary material, which is available to authorized users.

High genetic diversity of Plasmodium falciparum in the low transmission setting of the Kingdom of Eswatini

“…Though this study contradicts previous studies from low transmission settings outside of sub-Saharan Africa, this apparent paradox between genetic diversity and transmission intensity is not surprising. Indeed, this finding is consistent with the known epidemiological patterns of malaria in Eswatini, where P. falciparum transmission needs to be considered in the context of substantial human mobility between Eswatini and neighboring high transmission countries [3,4,7], which has resulted in the frequent importation of highly diverse parasites into a setting of limited local transmission [44,45].…”

“…In these areas, malaria transmission is unstable and highly dependent on rainfall and cross-border malaria importation [5,7,24].…”

“…A key challenge in eliminating malaria in Eswatini has been cross-border migration from neighboring, high transmission countries, which has resulted in malaria importation and sporadic onward transmission in malaria receptive areas [3][4][5]. Between 2014 and 2016, a total of 55% of infections were classified as imported [6], mainly from Mozambique (90%) [4,7].…”

Mapping the travel patterns of people with malaria in Bangladesh

“…However, the malaria status of participants in this African study was unknown and the geographical context is very different from Bangladesh with a much larger study area with more long-distance travel. A recent study conducted in Swaziland in an area of low malaria transmission looked at travellers stratified by their malaria status and found a gender bias, with males of working aged 25-44 years to be at higher risk of contracting malaria when crossing international borders [67].…”

Comparing Metapopulation Dynamics of Infectious Diseases under Different Models of Human Movement

“…Census data attempts to describe population migration that occurs between census years (8). Traditional surveys of commuters (9,10), patients (11,12), or residents in disease-affected areas (13,14) provide another description of recent travel activity. More recently, mobile phone service providers have shared privacy-protected data sets, showing how large numbers of users tend to move between cell phone towers (12,(15)(16)(17)(18).…”