Quantifying and monitoring the spatial and temporal dynamics of the global land cover is critical for better understanding many of the Earth’s land surface processes. However, the lack of regularly updated, continental-scale, and high spatial resolution (30 m) land cover data limit our ability to better understand the spatial extent and the temporal dynamics of land surface changes. Despite the free availability of high spatial resolution Landsat satellite data, continental-scale land cover mapping using high resolution Landsat satellite data was not feasible until now due to the need for high-performance computing to store, process, and analyze this large volume of high resolution satellite data. In this study, we present an approach to quantify continental land cover and impervious surface changes over a long period of time (15 years) using high resolution Landsat satellite observations and Google Earth Engine cloud computing platform. The approach applied here to overcome the computational challenges of handling big earth observation data by using cloud computing can help scientists and practitioners who lack high-performance computational resources.

BackgroundLao People Democratic Republic (PDR; Laos), a landlocked country in Southeast Asia, has made important progress in reducing malaria morbidity and mortality in the past 5–6 years, and the northern provinces have very low reported incidence. To support national progress towards elimination, it is critical to verify and understand these changes in disease burden.MethodsA two-stage cluster cross-sectional survey was conducted in four districts within four northern provinces (Khua, Phongsaly Province; Paktha, Bokeo Province; Nambak, Luang Prabang, and Muang Et, Huaphanh Province). During September and October 2016, demographics and malaria risk factors were collected from a total of 1492 households. A total of 5085 persons consented to collection of blood samples for testing, by rapid diagnostic test (RDT) and polymerase chain reaction (PCR)-based testing. Risk factors for infection were examined using logistic regression; and a randomized subset of males was tested for glucose-6-phosphate dehydrogenase (G6PD) deficiencies using a combined PCR and sequencing approach.ResultsThere were zero positives by RDT, and PCR detected Plasmodium infections in 39 (0.77%; 95% CI 0.40–1.47%) of 5082 analysable samples. The species distribution was Plasmodium vivax (28 total); Plasmodium falciparum/P. vivax (5); P. falciparum (3), Plasmodium malariae (2), and P. vivax/P. malariae (1). In multivariable analysis, the main risk factors included having any other cases within the household [aOR 12.83 (95% CI 4.40 to 37.38), p < 0.001]; and lack of bed net ownership within the household [aOR 10.91 (95% 5.42–21.94), p < 0.001]; age, sex and forest-travel were not associated with parasitaemia. A total of 910 males were tested for the six most common G6PDd in SE Asia; and 30 (3.3%; 95% CI 2.1–5.1%) had a G6PD variant allele associated with G6PD deficiency, with the majority being the Union (14) and Viangchan (11) polymorphisms, with smaller numbers of Canton and Mahidol.ConclusionThis is the first rigorous PCR-based population survey for malaria infection in Northern Lao PDR, and found a very low prevalence of asymptomatic Plasmodium infections by standard PCR methods, with P. vivax predominating in the surveyed districts. Clustering of cases within households, and lack of a bed nets suggest reactive case detection, and scale-up of coverage should be prioritized. The predominance of infections with P. vivax, combined with moderate levels of serious G6PD deficiencies highlight the need for careful rollout of primaquine towards elimination goals.Electronic supplementary materialThe online version of this article (10.1186/s12936-018-2367-5) contains supplementary material, which is available to authorized users.

Objectives To improve our understanding of climate variability and diarrheal disease at the community level and inform predictions for future climate change scenarios, we examined whether the El Niño climate pattern is associated with increased rates of diarrhea among Peruvian children. Methods We analyzed daily surveillance data for 367 children aged 0 to 12 years from 2 cohorts in a peri-urban shantytown in Lima, Peru, 1995 through 1998. We stratified diarrheal incidence by 6-month age categories, season, and El Niño, and modeled between-subject heterogeneity with random effects Poisson models. Results Spring diarrheal incidence increased by 55% during El Niño compared with before El Niño. This increase was most acute among children older than 60 months, for whom the risk of a diarrheal episode during the El Niño spring was nearly 100% greater (relative risk = 1.96; 95% confidence interval = 1.24, 3.09). Conclusions El Niño–associated climate variability affects community rates of diarrhea, particularly during the cooler seasons and among older children. Public health officials should develop preventive strategies for future El Niño episodes to mitigate the increased risk of diarrheal disease in vulnerable communities.

As countries in the Greater Mekong Sub-region (GMS) increasingly focus their malaria control and elimination efforts on reducing forest-related transmission, greater understanding of the relationship between deforestation and malaria incidence will be essential for programs to assess and meet their 2030 elimination goals. Leveraging village-level health facility surveillance data and forest cover data in a spatio-temporal modeling framework, we found evidence that deforestation is associated with short-term increases, but long-term decreases in confirmed malaria case incidence in Lao People's Democratic Republic (Lao PDR). We identified strong associations with deforestation measured within 30 km of villages but not with deforestation in the near (10 km) and immediate (1 km) vicinity. Results appear driven by deforestation in densely forested areas and were more pronounced for infections with Plasmodium falciparum (P. falciparum) than for Plasmodium vivax (P. vivax). These findings highlight the influence of forest activities on malaria transmission in the GMS.