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Mapping infectious disease landscapes: unmanned aerial vehicles and epidemiology

Fornace KM, Drakeley CJ, William T, Espino F, Cox J

Trends Parasitol, 2014 Nov;30(11):514-9.
PMID: 25443854 DOI: 10.1016/j.pt.2014.09.001

The potential applications of unmanned aerial vehicles (UAVs), or drones, have generated intense interest across many fields. UAVs offer the potential to collect detailed spatial information in real time at relatively low cost and are being used increasingly in conservation and ecological research. Within infectious disease epidemiology and public health research, UAVs can provide spatially and temporally accurate data critical to understanding the linkages between disease transmission and environmental factors. Using UAVs avoids many of the limitations associated with satellite data (e.g., long repeat times, cloud contamination, low spatial resolution). However, the practicalities of using UAVs for field research limit their use to specific applications and settings. UAVs fill a niche but do not replace existing remote-sensing methods.
Fulltext Use of mobile technology-based participatory mapping approaches to geolocate health facility attendees for disease surveillance in low resource settings

Fornace KM, Surendra H, Abidin TR, Reyes R, Macalinao MLM, Stresman G, et al.

Int J Health Geogr, 2018 06 18;17(1):21.
PMID: 29914506 DOI: 10.1186/s12942-018-0141-0

BACKGROUND: Identifying fine-scale spatial patterns of disease is essential for effective disease control and elimination programmes. In low resource areas without formal addresses, novel strategies are needed to locate residences of individuals attending health facilities in order to efficiently map disease patterns. We aimed to assess the use of Android tablet-based applications containing high resolution maps to geolocate individual residences, whilst comparing the functionality, usability and cost of three software packages designed to collect spatial information.
RESULTS: Using Open Data Kit GeoODK, we designed and piloted an electronic questionnaire for rolling cross sectional surveys of health facility attendees as part of a malaria elimination campaign in two predominantly rural sites in the Rizal, Palawan, the Philippines and Kulon Progo Regency, Yogyakarta, Indonesia. The majority of health workers were able to use the tablets effectively, including locating participant households on electronic maps. For all households sampled (n = 603), health facility workers were able to retrospectively find the participant household using the Global Positioning System (GPS) coordinates and data collected by tablet computers. Median distance between actual house locations and points collected on the tablet was 116 m (IQR 42-368) in Rizal and 493 m (IQR 258-886) in Kulon Progo Regency. Accuracy varied between health facilities and decreased in less populated areas with fewer prominent landmarks.
CONCLUSIONS: Results demonstrate the utility of this approach to develop real-time high-resolution maps of disease in resource-poor environments. This method provides an attractive approach for quickly obtaining spatial information on individuals presenting at health facilities in resource poor areas where formal addresses are unavailable and internet connectivity is limited. Further research is needed on how to integrate these with other health data management systems and implement in a wider operational context.
Fulltext Exposure and infection to Plasmodium knowlesi in case study communities in Northern Sabah, Malaysia and Palawan, The Philippines

Fornace KM, Herman LS, Abidin TR, Chua TH, Daim S, Lorenzo PJ, et al.

PLoS Negl Trop Dis, 2018 Jun;12(6):e0006432.
PMID: 29902171 DOI: 10.1371/journal.pntd.0006432

BACKGROUND: Primarily impacting poor, rural populations, the zoonotic malaria Plasmodium knowlesi is now the main cause of human malaria within Malaysian Borneo. While data is increasingly available on symptomatic cases, little is known about community-level patterns of exposure and infection. Understanding the true burden of disease and associated risk factors within endemic communities is critical for informing evidence-based control measures.
METHODOLOGY/PRINCIPAL FINDINGS: We conducted comprehensive surveys in three areas where P. knowlesi transmission is reported: Limbuak, Pulau Banggi and Matunggung, Kudat, Sabah, Malaysia and Bacungan, Palawan, the Philippines. Infection prevalence was low with parasites detected by PCR in only 0.2% (4/2503) of the population. P. knowlesi PkSERA3 ag1 antibody responses were detected in 7.1% (95% CI: 6.2-8.2%) of the population, compared with 16.1% (14.6-17.7%) and 12.6% (11.2-14.1%) for P. falciparum and P. vivax. Sero-prevalence was low in individuals <10 years old for P. falciparum and P. vivax consistent with decreased transmission of non-zoonotic malaria species. Results indicated marked heterogeneity in transmission intensity between sites and P. knowlesi exposure was associated with agricultural work (OR 1.63; 95% CI 1.07-2.48) and higher levels of forest cover (OR 2.40; 95% CI 1.29-4.46) and clearing (OR 2.14; 95% CI 1.35-3.40) around houses. Spatial patterns of P. knowlesi exposure differed from exposure to non-zoonotic malaria and P. knowlesi exposed individuals were younger on average than individuals exposed to non-zoonotic malaria.
CONCLUSIONS/SIGNIFICANCE: This is the first study to describe serological exposure to P. knowlesi and associated risk factors within endemic communities. Results indicate community-level patterns of infection and exposure differ markedly from demographics of reported cases, with higher levels of exposure among women and children. Further work is needed to understand these variations in risk across a wider population and spatial scale.
Fulltext An open dataset of Plasmodium vivax genome variation in 1,895 worldwide samples

MalariaGEN, Adam I, Alam MS, Alemu S, Amaratunga C, Amato R, et al.

Wellcome Open Res, 2022;7:136.
PMID: 35651694 DOI: 10.12688/wellcomeopenres.17795.1

This report describes the MalariaGEN Pv4 dataset, a new release of curated genome variation data on 1,895 samples of Plasmodium vivax collected at 88 worldwide locations between 2001 and 2017. It includes 1,370 new samples contributed by MalariaGEN and VivaxGEN partner studies in addition to previously published samples from these and other sources. We provide genotype calls at over 4.5 million variable positions including over 3 million single nucleotide polymorphisms (SNPs), as well as short indels and tandem duplications. This enlarged dataset highlights major compartments of parasite population structure, with clear differentiation between Africa, Latin America, Oceania, Western Asia and different parts of Southeast Asia. Each sample has been classified for drug resistance to sulfadoxine, pyrimethamine and mefloquine based on known markers at the dhfr, dhps and mdr1 loci. The prevalence of all of these resistance markers was much higher in Southeast Asia and Oceania than elsewhere. This open resource of analysis-ready genome variation data from the MalariaGEN and VivaxGEN networks is driven by our collective goal to advance research into the complex biology of P. vivax and to accelerate genomic surveillance for malaria control and elimination.