Biting mosquitos spread the malaria parasite to humans. Along the Mekong River in Southeast Asia, spending time in the surrounding forest increases a person's risk of malaria. This has led to a debate about whether deforestation in this area, which is called the Greater Mekong Sub-region (GMS), will increase or decrease malaria transmission. The answer to the debate is not clear because some malaria-transmitting mosquitos thrive in heavily forested areas, in particular in the GMS, while others prefer less forested areas.

Scientists studying malaria in the Amazon in South America suspect that malaria transmission increases shortly after deforestation but decreases six to eight years later. Some studies have tested this ‘frontier malaria’ theory but the results have been conflicting. Fewer studies have tested this theory in Southeast Asia. But deforestation has been blamed for recent malaria outbreaks in the GMS.

Using data on malaria testing and forest cover in the GMS, Rerolle et al. show that deforestation around villages increases malaria transmission in the first two years and decreases malaria rates later. This trend was driven mostly by a type of malaria called Plasmodium falciparum and was less strong for Plasmodium vivax. The location of deforested areas also mattered. Deforestation within one to 10 kilometer of villages did not affect malaria rates. Deforestation further away in about a 30 kilometer radius did affect malaria transmission. Rerolle et al. suggest this may be because villagers have to spend longer times trekking through forests to hunt or harvest wood when the wider area is deforested.

Currently, National Malaria Control Programs in the GMS focus their efforts on reducing forest-related transmission. This study strengthens the evidence supporting this approach. The results also suggest that different malaria elimination strategies may be necessary for different types of malaria parasite. Using this new information could help malaria control programs better target resources or educate villagers on how to protect themselves. The innovative methods used by Rerolle et al. reveal a more complex role of deforestation in malaria transmission and may inspire other scientists to think more carefully about environmental drivers of malaria.

Engaging in forest activities, such as logging, hunting, or spending the night in the forest, is considered a primary risk factor for malaria infection in the Greater Mekong Sub-region (GMS) (Chaveepojnkamjorn and Pichainarong, 2004; Das et al., 2004; Lansang et al., 1997; Erhart et al., 2005; Trung et al., 2004) with recent outbreaks attributed to deforestation activities (Lyttleton, 2016). This is most likely the result of increased human exposure to the forest dwelling Anopheles dirus and Anopheles minimus, the most efficient and widespread malaria vectors in the GMS (Obsomer et al., 2007; Obsomer et al., 2013). However, deforestation may also alter this ‘forest malaria’ (Sharma and Kondrashin, 1991) ecosystem and eventually reduce malaria incidence, as is generally accepted to be the case in Southeast Asia (Guerra et al., 2006). Several previous studies have assessed the relationship between deforestation and malaria, but the majority focused outside of the GMS, most notably in the Amazonian forest (Wayant et al., 2010; Olson et al., 2010; Hahn et al., 2014a; Valle and Clark, 2013; Terrazas et al., 2015) where the evidence has been met with conflicting interpretations (Tucker Lima et al., 2017). As national malaria programs across the GMS target forest-going populations for prevention and treatment efforts (Guyant et al., 2015; WHO, 2016), improved understanding of the relationship between deforestation and malaria is critical for programs to assess and meet national 2030 malaria elimination goals (World Health Organization, 2016; WHO, 2018).

In the Amazon, the ‘frontier malaria’ hypothesis (Sawyer, 1988) posits that malaria temporarily increases following deforestation efforts to open a human settlement area in the forest. Subsequently, after approximately 6–8 years, settlements become more urbanized and isolated from the surrounding forest, and less suitable for malaria vectors, resulting in reduced malaria transmission (de Castro et al., 2006). Recent work has challenged this hypothesis, however, and found that some older settlements were also likely to have high malaria incidence (Ilacqua et al., 2018), highlighting the importance of assessing the relationship between deforestation and malaria at different spatio-temporal scales (Singer and de Castro, 2006).

A recent review of the literature on deforestation and malaria in the Amazon (Tucker Lima et al., 2017) recommended the integration of multiple socio-economic, demographic and ecological mechanisms to disentangle the relationship between deforestation and malaria. The complexity of land-use changes driving deforestation such as urbanization, agriculture, irrigation or resource mining can alter the environment in different ways. For example, deforestation in the Amazon has been shown to increase mosquitoes’ larval habitat (Vittor et al., 2009) through the creation of areas with abundant sunlight and pooling water, resulting in increased human biting activity (Vittor et al., 2006). Alternatively, immigration and rapid population movements, stirring human-vector interactions, are other mechanisms affecting malaria transmission in frontier areas (Moreno et al., 2007). A modeling study (Baeza et al., 2017) showed that the temporal pattern of increased incidence followed by a decrease can vary depending on ecological and socio-economic parameters in frontiers areas.

The importance of addressing complex confounding structures influencing the relationship between deforestation and malaria was also highlighted by Bauhoff and Busch, 2020. Variables such as temperature (Beck-Johnson et al., 2013; Mordecai et al., 2013), precipitation (Parham and Michael, 2010; Parham et al., 2012), or seasonality Hay et al., 1998 are known environmental predictors of malaria, although the spatio-temporal scale of those effects often varies across different areas (Teklehaimanot et al., 2004). Furthermore, remote areas may experience higher malaria rates because of poor access to public health services, but also have denser forest cover or lower deforestation rates (Busch and Ferretti-Gallon, 2017). Finally, forest-going populations in the GMS are also at higher risk for malaria (Parker et al., 2017) due to poor adherence to protective measures against mosquitoes such as insecticide-treated bed nets (ITNs) or long-lasting insecticidal hammocks (LLIHs) (Peeters Grietens et al., 2010; WHO, 2016) and inadequate access to treatment (Guyant et al., 2015).

Bauhoff and Busch, 2020 identified only 10 empirical studies that assessed the relationship between deforestation and malaria with appropriate adjustments for confounding. Of these, seven reported a positive association (Austin et al., 2017; Wayant et al., 2010; Olson et al., 2010; Terrazas et al., 2015; Pattanayak et al., 2010; Garg, 2015; Fornace et al., 2016), two did not find any associations (Bauhoff and Busch, 2020; Hahn et al., 2014a), and one disputed study found a negative association (Valle and Clark, 2013; Hahn et al., 2014b; Valle, 2014). Most recently, a study found deforestation to increase malaria risk and malaria to decrease deforestation activities in the Amazon, using an instrumental variable analysis to disentangle any reverse causality loop (MacDonald and Mordecai, 2019). However, only half of the above-mentioned studies used high-resolution forest data, with most studies using spatially aggregated data and exploring only a limited range of spatial and temporal scales. Only three of these studies were conducted in Southeast Asia (Pattanayak et al., 2010; Garg, 2015; Fornace et al., 2016), and none in the GMS. Importantly, all three found that malaria increases after deforestation, but all had limitations. The two studies in Indonesia Pattanayak et al., 2010; Garg, 2015 used coarsely aggregated forest data and potentially biased self-reported malaria data. The third study, in Malaysia (Fornace et al., 2016), focused on a specific and geographically confined malaria parasite, Plasmodium knowlesi, whose primary host is the long-tailed macaque and whose presence in the GMS, where P. falciparum and P. vivax dominate, is limited.

In this analysis, we examined the relationship between deforestation and malaria incidence by combining high-resolution forest coverage data (Hansen et al., 2013) and monthly malaria incidence data from 2013 to 2016 from two separate regions in the GMS: northern Lao People’s Democratic Republic (PDR) with very low malaria transmission and southern Lao PDR where P. falciparum and P. vivax are seasonal. By conducting the analysis at the village level, we were able to explore a wide range of spatial scales (1, 10, and 30 km around villages) that might be relevant in characterizing the relationship between deforestation and malaria. In addition, we leveraged the longitudinal nature of both the incidence data collected and the forest data produced from annual remote sensing imagery (Hansen et al., 2013) to explore the most relevant temporal scales. Finally, we considered alternative definitions of deforestation, restricted to areas with at least certain levels of forest cover, to investigate the type of deforestation driving the relationship with malaria.

To date, no prior studies have quantified the relationship between deforestation and malaria incidence in the GMS. Understanding this relationship is especially important in the GMS, where forest-going activities are a main source of income generation (Lao PDR, 2016) and malaria clusters in forest-going populations (World Health Organization, 2016; WHO, 2016). To assess the relationship between deforestation and malaria incidence, we modeled the monthly village-level malaria incidence in two regions of Lao PDR using health facility surveillance data and evaluated the most relevant spatio-temporal scale.

Based on a large dataset of health facility surveillance records in two regions of Lao PDR, we found evidence that deforestation around villages is associated with higher malaria incidence over the short-term but lower incidence over the long-term (e.g, in the south, within 30 km of villages: IRR = 1.16 [1.10; 1.22] for deforestation in the previous year and IRR = 0.93 [0.90; 0.97] for deforestation in the previous 3 years). Our evaluation of alternative spatial scales identified strong associations for deforestation within a 30 km radius around villages but not for deforestation in the near (10 km) and immediate (1 km) vicinity. Our results incorporated correction for the probability of seeking treatment, modeled as a function of distance to the closest health facility, as well as adjustment for several environmental covariates. Results appear driven by deforestation in densely forested areas and the patterns exhibited are clearer for infections with P. falciparum than for P. vivax.

The wide availability and longitudinal nature of malaria surveillance records collected routinely by the national program enabled exploration of the relationship between deforestation and malaria incidence over multiple spatio-temporal scales and across different levels of forest density. The spatio-temporal variability highlighted here provides insights into the causal mechanisms driving local-scale malaria incidence in the GMS. This approach not only quantified the deforestation-malaria incidence association in the GMS, but also strengthened the evidence for the key influence of forest-going populations on malaria transmission in the GMS.

This study’s results echo the frontier malaria hypothesis from the Amazon region, which posits an increase in malaria incidence in the first few years following deforestation and a decrease over the long term. However, we found an earlier inflexion point, 1–3 years after deforestation compared to 6–8 years in the Amazon (de Castro et al., 2006), most likely because of very different underlying human processes. Indeed, the frontier malaria hypothesis considers non-indigenous human settlements sprouting deeper and deeper in the forest whereas forest-going populations in the GMS are primarily members of established forest-fringe communities who regularly tour the forest overnight to hunt and collect wood (Dysoley et al., 2008). Industrial and agricultural projects or lucrative forest-based activities also attract mobile and migrant populations (MMPs) Guyant et al., 2015 in remote forested areas of the GMS but not on the same scale as the politically and economically driven unique colonization of the Amazon (de Castro et al., 2006).

Our results are also consistent with the three previous multivariable empirical studies (Garg, 2015; Pattanayak et al., 2010; Fornace et al., 2016) that assessed the effect of deforestation on malaria in Southeast Asia. Our study builds on these findings by using higher resolution forest data and exploring additional spatio-temporal scales. Using biennial village census data from Indonesia between 2003 and 2008 and district-aggregated remote sensing forest data, Garg, 2015 reported a 2–10.4% increase in the probability of a malaria outbreak in each village of districts that lost 1000 hectares of their forest cover in the same year. Using data from a 1996 cross-sectional household survey conducted in a quasi-experimental setting around a protected area in Indonesia, Pattanayak et al., 2010 found a positive association between disturbed forest (vs undisturbed) and malaria in children under 5, again using no temporal lag. Our analysis plan was largely inspired by Fornace et al., 2016, which used similar high-resolution forest data (Hansen et al., 2013) and 2008–2012 incidence data from Sabah, Malaysia. They reported a 2.22 (95% CI: [1.53; 2.93]) increase in the P. knowlesi incidence rate for villages where more than 14% (<8%, being the reference) of the surrounding area (within 2 km) experienced forest loss in the previous 5 years. On the other hand, our analysis explored wider spatial scales, bypassed any coarse categorization of forest and deforestation variables, corrected incidence for treatment-seeking probability, and most importantly focused on P. falciparum and P. vivax, the dominant malaria parasites in the GMS.

Engaging in forest activities, such as logging, hunting or spending the night in the forest, has been reported as a major risk factor by many studies in the region (Chaveepojnkamjorn and Pichainarong, 2004; Das et al., 2004; Lansang et al., 1997; Erhart et al., 2005; Trung et al., 2004). As countries of the GMS work toward malaria elimination, the literature stresses the key role of forest-going populations (Guyant et al., 2015; Nofal et al., 2019; Bannister-Tyrrell et al., 2019; Wen et al., 2016; Smith and Whittaker, 2014), although research programs highlight the challenges of accessing them (Bennett et al., 2021 ; Lover et al., 2019) as well as their diversity (Nofal et al., 2019; Bannister-Tyrrell et al., 2019). To our knowledge, no previous study has leveraged geo-spatial statistical analyses to characterize the importance of forest-going populations in the GMS. Our results suggest that deforestation in dense forests (Table 3) around villages, particularly areas further from the village (Table 1), is a driver of malaria in Lao PDR. We argue that this is indicative of the existence of a key high-risk group linking the deforestation patterns identified to malaria in the villages, namely a forest-going population. Deforestation captured by remote sensing in this setting likely reflects locations and times of heightened activity in the forest areas near villages, and therefore greater human-vector contact. We suspect longer and deeper trips into the forest result in increased exposure to mosquitoes, putting forest-goers at higher risk.

We conducted this study in northern and southern Lao PDR, where the malaria species composition differs, and assessed species-specific relationships in the south where P. falciparum and P. vivax are co-endemic. Our results highlight the challenges ahead of national programs with P. vivax elimination after successful P. falciparum elimination, as increasingly mentioned in the literature (Cotter et al., 2013; Kaehler et al., 2019). This study identified a clear pattern of spatio-temporal associations between P. falciparum and deforestation, but these were not apparent for P. vivax (Table 2). The increase in P. vivax incidence in the first 2 years following deforestation was identified as well but the associations were smaller than for P. falciparum. Importantly, deforestation was never associated with lower risks of P. vivax. A recent study in the Amazon MacDonald and Mordecai, 2019 reported a similar attenuation of the effects of deforestation on P. vivax compared to P. falciparum, most likely because of P. vivax parasites’ ability to relapse months or even years after infection, which decouples the association between transmission and incidence data. These species-specific differences may also explain why the pattern of spatio-temporal associations between malaria and deforestation were markedly clearer in the south than in the north where P. vivax dominates.

Our results did have some inherent limitations based upon routine health facility surveillance data. First, reliability of such records varies across and within countries of the GMS and may depend on malaria incidence level. This could lead to unmeasured residual confounding, further exacerbated by the lack of available data on malaria control activities in the region. Another challenge with these data is obtaining an accurate denominator for incidence, as not everyone attends a public health center when febrile. We addressed this issue by modeling the probability of seeking treatment as a function of travel time to the closest health facility using data from two cross-sectional surveys. Last, the village-level geo-referencing of malaria registries ignores the possibility that patients may become infected elsewhere. Unfortunately, these surveillance records did not include information about patients’ forest-going trips. Research to track and analyse micro-scale movements of forest-goers is needed to understand how they interact with the forest and where are the foci of infection.

The forest data we used has also been criticized, in particular for not distinguishing tropical forests from agroforestry (Tropek et al., 2014; Hansen et al., 2014) or man-made from natural causes of deforestation. The lack of temporal resolution for the forest gain variable (2000–2017 aggregate) as well as the assumption that forest loss happens all in 1 year are additional limitations of these data. Finally, our relative measure of deforestation, key to consistently compare the effects across different spatial scales, also implies that a 0.1% of the area that experienced forest loss within 30 km of a village is a much larger area (∼280 hectares) than within 1 km (∼0.3 hectare) and should be interpreted cautiously.

In conclusion, this study assessed the relationship between deforestation and malaria in Lao PDR. Our approach leveraged surveillance records collected by the national malaria program and high-resolution forest data and rigorously explored the spatio-temporal pattern of associations. As countries of the GMS work toward malaria elimination, our results highlight the challenges to transition from P. falciparum to P. vivax elimination, confirm and characterize the importance of high-risk populations engaging in forest activities and suggest malaria programs may benefit from monitoring areas of on-going deforestation using remotely sensed data.

All data generated or analysed during this study are included in the manuscript and supporting files. Source data files have been provided for Figures 2, 3, 4 and 5 and for Tables 1, 2 and 3.

1. 1. Alegana VA
   2. Wright JA
   3. Pentrina U
   4. Noor AM
   5. Snow RW
   6. Atkinson PM

   (2012) Spatial modelling of healthcare utilisation for treatment of fever in Namibia

   International Journal of Health Geographics 11:6.

   https://doi.org/10.1186/1476-072X-11-6

   • PubMed
   • Google Scholar
2. 1. Austin KF
   2. Bellinger MO
   3. Rana P

   (2017) Anthropogenic forest loss and malaria prevalence: a comparative examination of the causes and disease consequences of deforestation in developing nations

   AIMS Environmental Science 4:217–231.

   https://doi.org/10.3934/environsci.2017.2.217

   • Google Scholar
3. 1. Baeza A
   2. Santos-Vega M
   3. Dobson AP
   4. Pascual M

   (2017) The rise and fall of malaria under land-use change in frontier regions

   Nature Ecology & Evolution 1:108.

   https://doi.org/10.1038/s41559-017-0108

   • PubMed
   • Google Scholar
4. 1. Bannister-Tyrrell M
   2. Gryseels C
   3. Sokha S
   4. Dara L
   5. Sereiboth N
   6. James N
   7. Thavrin B
   8. Ly P
   9. Soy Ty K
   10. Peeters Grietens K
   11. Sovannaroth S
   12. Yeung S

   (2019) Forest goers and Multidrug-Resistant malaria in Cambodia: an ethnographic study

   The American Journal of Tropical Medicine and Hygiene 100:1170–1178.

   https://doi.org/10.4269/ajtmh.18-0662

   • PubMed
   • Google Scholar
5. 1. Bauhoff S
   2. Busch J

   (2020) Does deforestation increase malaria prevalence? evidence from satellite data and health surveys

   World Development 127:104734.

   https://doi.org/10.1016/j.worlddev.2019.104734

   • Google Scholar
6. 1. Beck-Johnson LM
   2. Nelson WA
   3. Paaijmans KP
   4. Read AF
   5. Thomas MB
   6. Bjørnstad ON

   (2013) The effect of temperature on anopheles mosquito population dynamics and the potential for malaria transmission

   PLOS ONE 8:e79276.

   https://doi.org/10.1371/journal.pone.0079276

   • PubMed
   • Google Scholar
7. Report

   1. Bennett A
   2. Lover A
   3. Dantzer EU

   (2021)

   Report on Formative Assessment to Identify and Characterize Mobile and Migrant Populations in Champasak Province, Southern Lao Pdr

   University of California, San Francisco (UCSF), Malaria Elimination Initiative, Lao PDR.

   • Google Scholar
8. 1. Busch J
   2. Ferretti-Gallon K

   (2017) What drives deforestation and what stops it? A Meta-Analysis

   Review of Environmental Economics and Policy 11:3–23.

   https://doi.org/10.1093/reep/rew013

   • Google Scholar
9. 1. Chaveepojnkamjorn W
   2. Pichainarong N

   (2004)

   Malaria infection among the migrant population along the thai-myanmar border area

   The Southeast Asian Journal of Tropical Medicine and Public Health 35:48–52.

   • PubMed
   • Google Scholar
10. 1. Cotter C
    2. Sturrock HJ
    3. Hsiang MS
    4. Liu J
    5. Phillips AA
    6. Hwang J
    7. Gueye CS
    8. Fullman N
    9. Gosling RD
    10. Feachem RG

    (2013) The changing epidemiology of malaria elimination: new strategies for new challenges

    The Lancet 382:900–911.

    https://doi.org/10.1016/S0140-6736(13)60310-4

    • PubMed
    • Google Scholar
11. 1. Csardi G
    2. Nepusz T

    (2006)

    The igraph software package for complex network research

    InterJournal, Complex Systems 1695:1–9.

    • Google Scholar
12. 1. Das NG
    2. Talukdar PK
    3. Das SC

    (2004)

    Epidemiological and entomological aspects of malaria in forest-fringed villages of sonitpur district, Assam

    Journal of Vector Borne Diseases 41:5.

    • PubMed
    • Google Scholar
13. 1. de Castro MC
    2. Monte-Mór RL
    3. Sawyer DO
    4. Singer BH

    (2006) Malaria risk on the amazon frontier

    PNAS 103:2452–2457.

    https://doi.org/10.1073/pnas.0510576103

    • PubMed
    • Google Scholar
14. 1. Dysoley L
    2. Kaneko A
    3. Eto H
    4. Mita T
    5. Socheat D
    6. Börkman A
    7. Kobayakawa T

    (2008) Changing patterns of forest malaria among the mobile adult male population in chumkiri district, Cambodia

    Acta Tropica 106:207–212.

    https://doi.org/10.1016/j.actatropica.2007.01.007

    • PubMed
    • Google Scholar
15. 1. Erhart A
    2. Ngo DT
    3. Phan VK
    4. Ta TT
    5. Van Overmeir C
    6. Speybroeck N
    7. Obsomer V
    8. Le XH
    9. Le KT
    10. Coosemans M
    11. D'alessandro U

    (2005) Epidemiology of forest malaria in central vietnam: a large scale cross-sectional survey

    Malaria Journal 4:58.

    https://doi.org/10.1186/1475-2875-4-58

    • PubMed
    • Google Scholar
16. Software

    1. European space agency

    (2009) Globcover Land Cover V2 2009 Database

    Globcover Land Cover V2 2009 Database.

    http://due.esrin.esa.int/page_globcover.php
17. 1. Fornace KM
    2. Abidin TR
    3. Alexander N
    4. Brock P
    5. Grigg MJ
    6. Murphy A
    7. William T
    8. Menon J
    9. Drakeley CJ
    10. Cox J

    (2016) Association between landscape factors and spatial patterns of Plasmodium knowlesi infections in sabah, Malaysia

    Emerging Infectious Diseases 22:201–209.

    https://doi.org/10.3201/eid2202.150656

    • PubMed
    • Google Scholar
18. 1. Funk CC
    2. Peterson PJ
    3. Landsfeld MF
    4. Pedreros DH
    5. Verdin JP
    6. Rowland JD
    7. Romero BE
    8. Husak GJ
    9. Michaelsen JC
    10. Verdin AP

    (2014) A quasi-global precipitation time series for drought monitoring

    US Geological Survey Data Series 832:1–12.

    https://doi.org/10.3133/ds832

    • Google Scholar
19. Website

    1. Garg T

    (2015) Public health effects of ecosystem degradation: evidence from deforestation in Indonesia

    Technical Report. Accessed March 8, 2015.

    https://www.sites.google.com/site/teevrat/research
20. 1. Guerra CA
    2. Snow RW
    3. Hay SI

    (2006) A global assessment of closed forests, deforestation and malaria risk

    Annals of Tropical Medicine and Parasitology 100:189–204.

    https://doi.org/10.1179/136485906X91512

    • PubMed
    • Google Scholar
21. 1. Guyant P
    2. Canavati SE
    3. Chea N
    4. Ly P
    5. Whittaker MA
    6. Roca-Feltrer A
    7. Yeung S

    (2015) Malaria and the mobile and migrant population in Cambodia: a population movement framework to inform strategies for malaria control and elimination

    Malaria Journal 14:252.

    https://doi.org/10.1186/s12936-015-0773-5

    • PubMed
    • Google Scholar
22. 1. Hahn MB
    2. Gangnon RE
    3. Barcellos C
    4. Asner GP
    5. Patz JA

    (2014a) Influence of deforestation, logging, and fire on malaria in the brazilian Amazon

    PLOS ONE 9:e85725.

    https://doi.org/10.1371/journal.pone.0085725

    • PubMed
    • Google Scholar
23. 1. Hahn MB
    2. Olson SH
    3. Vittor AY
    4. Barcellos C
    5. Patz JA
    6. Pan W

    (2014b) Conservation efforts and malaria in the brazilian Amazon

    The American Journal of Tropical Medicine and Hygiene 90:591–594.

    https://doi.org/10.4269/ajtmh.13-0323

    • PubMed
    • Google Scholar
24. 1. Hansen MC
    2. Potapov PV
    3. Moore R
    4. Hancher M
    5. Turubanova SA
    6. Tyukavina A
    7. Thau D
    8. Stehman SV
    9. Goetz SJ
    10. Loveland TR
    11. Kommareddy A
    12. Egorov A
    13. Chini L
    14. Justice CO
    15. Townshend JR

    (2013) High-resolution global maps of 21st-century forest cover change

    Science 342:850–853.

    https://doi.org/10.1126/science.1244693

    • PubMed
    • Google Scholar
25. 1. Hansen M
    2. Potapov P
    3. Margono B
    4. Stehman S
    5. Turubanova S
    6. Tyukavina A

    (2014) Response to comment on "High-resolution global maps of 21st-century forest cover change"

    Science 344:981.

    https://doi.org/10.1126/science.1248817

    • PubMed
    • Google Scholar
26. 1. Hay SI
    2. Snow RW
    3. Rogers DJ

    (1998) Predicting malaria seasons in Kenya using multitemporal meteorological satellite sensor data

    Transactions of the Royal Society of Tropical Medicine and Hygiene 92:12–20.

    https://doi.org/10.1016/S0035-9203(98)90936-1

    • PubMed
    • Google Scholar
27. 1. Ilacqua RC
    2. Chaves LSM
    3. Bergo ES
    4. Conn JE
    5. Sallum MAM
    6. Laporta GZ

    (2018) A method for estimating the deforestation timeline in rural settlements in a scenario of malaria transmission in frontier expansion in the Amazon region

    Memórias Do Instituto Oswaldo Cruz 113:e170522.

    https://doi.org/10.1590/0074-02760170522

    • Google Scholar
28. Software

    1. Jarvis A
    2. Reuter H
    3. Nelson A
    4. Guevara E

    (2008) Hole-Filled Srtm for the Globe Version , version 4

    Hole-Filled Srtm for the Globe Version .

    http://srtm.csi.cgiar.org
29. 1. Kaehler N
    2. Adhikari B
    3. Cheah PY
    4. von Seidlein L
    5. Day NPJ
    6. Paris DH
    7. Tanner M
    8. Pell C

    (2019) Prospects and strategies for malaria elimination in the greater mekong Sub-region: a qualitative study

    Malaria Journal 18:203.

    https://doi.org/10.1186/s12936-019-2835-6

    • PubMed
    • Google Scholar
30. 1. Lansang MA
    2. Belizario VY
    3. Bustos MD
    4. Saul A
    5. Aguirre A

    (1997) Risk factors for infection with malaria in a low endemic community in Bataan, the philippines

    Acta Tropica 63:257–265.

    https://doi.org/10.1016/S0001-706X(96)00625-0

    • PubMed
    • Google Scholar
31. Website

    1. Lao national census

    (2020) Lao decide

    Accessed January 15, 2020.

    http://www.decide.la/account/signin
32. Website

    1. Lao national malaria database (dhis2)

    (2018) Vientiane: cmpe/ministry of health

    Accessed January 15, 2020.

    https://hmis.gov.la/dhis-web-commons/security/login.action
33. Report

    1. Lao PDR

    (2016)

    Lao Pdr National Strategic Plan for Malaria Control and Elimination 2016-2020. Technical Report, Centre for Malariology, Parasitology, and Entomology (CMPE)

    Department of Communicable Disease Control, Lao PDR Ministry of Health. Vientiane, Lao PDR.

    • Google Scholar
34. 1. Lover AA
    2. Dantzer E
    3. Hongvanthong B
    4. Chindavongsa K
    5. Welty S
    6. Reza T
    7. Khim N
    8. Menard D
    9. Bennett A

    (2018) Prevalence and risk factors for asymptomatic malaria and genotyping of glucose 6-phosphate (G6PD) deficiencies in a vivax-predominant setting, lao PDR: implications for sub-national elimination goals

    Malaria Journal 17:218.

    https://doi.org/10.1186/s12936-018-2367-5

    • PubMed
    • Google Scholar
35. 1. Lover AA
    2. Dantzer E
    3. Hocini S
    4. Estera R
    5. Rerolle F
    6. Smith JL
    7. Hwang J
    8. Gosling R
    9. Yukich J
    10. Greenhouse B
    11. Jacobson J
    12. Phetsouvanh R
    13. Hongvanthong B
    14. Bennett A

    (2019) Study protocol for a cluster-randomized split-plot design trial to assess the effectiveness of targeted active malaria case detection among high-risk populations in southern lao PDR (the AcME-Lao study)

    Gates Open Research 3:1730.

    https://doi.org/10.12688/gatesopenres.13088.1

    • PubMed
    • Google Scholar
36. 1. Lyttleton C

    (2016) Deviance and resistance: malaria elimination in the greater mekong subregion

    Social Science & Medicine 150:144–152.

    https://doi.org/10.1016/j.socscimed.2015.12.033

    • PubMed
    • Google Scholar
37. 1. MacDonald AJ
    2. Mordecai EA

    (2019) Amazon deforestation drives malaria transmission, and malaria burden reduces forest clearing

    PNAS 116:22212–22218.

    https://doi.org/10.1073/pnas.1905315116

    • PubMed
    • Google Scholar
38. 1. Mordecai EA
    2. Paaijmans KP
    3. Johnson LR
    4. Balzer C
    5. Ben-Horin T
    6. de Moor E
    7. McNally A
    8. Pawar S
    9. Ryan SJ
    10. Smith TC
    11. Lafferty KD

    (2013) Optimal temperature for malaria transmission is dramatically lower than previously predicted

    Ecology Letters 16:22–30.

    https://doi.org/10.1111/ele.12015

    • PubMed
    • Google Scholar
39. 1. Moreno JE
    2. Rubio-Palis Y
    3. Páez E
    4. Pérez E
    5. Sánchez V

    (2007) Abundance, biting behaviour and parous rate of anopheline mosquito species in relation to malaria incidence in gold-mining Areas of southern Venezuela

    Medical and Veterinary Entomology 21:339–349.

    https://doi.org/10.1111/j.1365-2915.2007.00704.x

    • PubMed
    • Google Scholar
40. 1. Nofal SD
    2. Peto TJ
    3. Adhikari B
    4. Tripura R
    5. Callery J
    6. Bui TM
    7. von Seidlein L
    8. Pell C

    (2019) How can interventions that target forest-goers be tailored to accelerate malaria elimination in the greater mekong subregion? A systematic review of the qualitative literature

    Malaria Journal 18:32.

    https://doi.org/10.1186/s12936-019-2666-5

    • PubMed
    • Google Scholar
41. 1. Obsomer V
    2. Defourny P
    3. Coosemans M

    (2007) The anopheles dirus complex: spatial distribution and environmental drivers

    Malaria Journal 6:26.

    https://doi.org/10.1186/1475-2875-6-26

    • PubMed
    • Google Scholar
42. 1. Obsomer V
    2. Dufrene M
    3. Defourny P
    4. Coosemans M

    (2013) Anopheles species associations in Southeast Asia: indicator species and environmental influences

    Parasites & Vectors 6:136.

    https://doi.org/10.1186/1756-3305-6-136

    • PubMed
    • Google Scholar
43. Report

    1. Okayas H

    (2018)

    Mekong Malaria Elimination Programme

    World Health Organization, Global Malaria Programme, Geneva.

    • Google Scholar
44. 1. Olson SH
    2. Gangnon R
    3. Silveira GA
    4. Patz JA

    (2010) Deforestation and malaria in mâncio Lima county, Brazil

    Emerging Infectious Diseases 16:1108–1115.

    https://doi.org/10.3201/eid1607.091785

    • PubMed
    • Google Scholar
45. Website

    1. Open street map

    (2020) Open street map

    Accessed January 15, 2020.

    http://www.openstreetmap.org
46. 1. Parham PE
    2. Pople D
    3. Christiansen-Jucht C
    4. Lindsay S
    5. Hinsley W
    6. Michael E

    (2012) Modeling the role of environmental variables on the population dynamics of the malaria vector anopheles gambiae sensu stricto

    Malaria Journal 11:271.

    https://doi.org/10.1186/1475-2875-11-271

    • PubMed
    • Google Scholar
47. 1. Parham PE
    2. Michael E

    (2010) Modeling the effects of weather and climate change on malaria transmission

    Environmental Health Perspectives 118:620–626.

    https://doi.org/10.1289/ehp.0901256

    • PubMed
    • Google Scholar
48. 1. Parker DM
    2. Tripura R
    3. Peto TJ
    4. Maude RJ
    5. Nguon C
    6. Chalk J
    7. Sirithiranont P
    8. Imwong M
    9. von Seidlein L
    10. White NJ
    11. Dondorp AM

    (2017) A multi-level spatial analysis of clinical malaria and subclinical plasmodium infections in Pailin province, Cambodia

    Heliyon 3:e00447.

    https://doi.org/10.1016/j.heliyon.2017.e00447

    • Google Scholar
49. Report

    1. Pattanayak SK
    2. Corey CG
    3. Lau YF
    4. Kramer RA

    (2010)

    Biodiversity Conservation and Child Malaria: Microeconomic Evidence From Flores, Indonesia

    Economic Research Initiatives at Duke Working Paper.

    • Google Scholar
50. 1. Peeters Grietens K
    2. Xuan XN
    3. Van Bortel W
    4. Duc TN
    5. Ribera JM
    6. Ba Nhat T
    7. Van KP
    8. Le Xuan H
    9. D'Alessandro U
    10. Erhart A

    (2010) Low perception of malaria risk among the Ra-glai ethnic minority in south-central vietnam: implications for forest malaria control

    Malaria Journal 9:23.

    https://doi.org/10.1186/1475-2875-9-23

    • PubMed
    • Google Scholar
51. Software

    1. R Development Core Team

    (2020) R: A Language and Environment for Statistical Computing

    R Foundation for Statistical Computing, Vienna, Austria.

    http://www.r-project.org
52. Report

    1. Sawyer DR

    (1988)

    Frontier Malaria in the Amazon Region of Brazil: Types of Malaria Situations and Some Implications for Control

    PHO/WHO/TDR.

    • Google Scholar
53. Conference

    1. Sharma VP
    2. Kondrashin AV

    (1991)

    Forest malaria in Southeast Asia

    Proceedings of an Informal Consultative Meeting WHO/MRC, WHO/Malaria Research Centre, New Delhi, 1991.

    • Google Scholar
54. 1. Singer B
    2. de Castro MC

    (2006) Enhancement and suppression of malaria in the Amazon

    The American Journal of Tropical Medicine and Hygiene 74:1–2.

    https://doi.org/10.4269/ajtmh.2006.74.1

    • PubMed
    • Google Scholar
55. 1. Smith C
    2. Whittaker M

    (2014) Beyond mobile populations: a critical review of the literature on malaria and population mobility and suggestions for future directions

    Malaria Journal 13:307.

    https://doi.org/10.1186/1475-2875-13-307

    • PubMed
    • Google Scholar
56. 1. Sturrock HJ
    2. Cohen JM
    3. Keil P
    4. Tatem AJ
    5. Le Menach A
    6. Ntshalintshali NE
    7. Hsiang MS
    8. Gosling RD

    (2014) Fine-scale malaria risk mapping from routine aggregated case data

    Malaria Journal 13:421.

    https://doi.org/10.1186/1475-2875-13-421

    • PubMed
    • Google Scholar
57. 1. Teklehaimanot HD
    2. Lipsitch M
    3. Teklehaimanot A
    4. Schwartz J

    (2004) Weather-based prediction of Plasmodium falciparum malaria in epidemic-prone regions of Ethiopia I. patterns of lagged weather effects reflect biological mechanisms

    Malaria Journal 3:41.

    https://doi.org/10.1186/1475-2875-3-41

    • PubMed
    • Google Scholar
58. 1. Terrazas WC
    2. Sampaio VS
    3. de Castro DB
    4. Pinto RC
    5. de Albuquerque BC
    6. Sadahiro M
    7. Dos Passos RA
    8. Braga JU

    (2015) Deforestation, drainage network, indigenous status, and geographical differences of malaria in the state of amazonas

    Malaria Journal 14:379.

    https://doi.org/10.1186/s12936-015-0859-0

    • PubMed
    • Google Scholar
59. 1. Tropek R
    2. Sedláček O
    3. Beck J
    4. Keil P
    5. Musilová Z
    6. Símová I
    7. Storch D

    (2014) Comment on "High-resolution global maps of 21st-century forest cover change"

    Science 344:981.

    https://doi.org/10.1126/science.1248753

    • PubMed
    • Google Scholar
60. 1. Trung HD
    2. Van Bortel W
    3. Sochantha T
    4. Keokenchanh K
    5. Quang NT
    6. Cong LD
    7. Coosemans M

    (2004) Malaria transmission and major malaria vectors in different geographical Areas of southeast asia

    Tropical Medicine and International Health 9:230–237.

    https://doi.org/10.1046/j.1365-3156.2003.01179.x

    • PubMed
    • Google Scholar
61. 1. Tucker Lima JM
    2. Vittor A
    3. Rifai S
    4. Valle D

    (2017) Does deforestation promote or inhibit malaria transmission in the Amazon? A systematic literature review and critical appraisal of current evidence

    Philosophical Transactions of the Royal Society B: Biological Sciences 372:20160125.

    https://doi.org/10.1098/rstb.2016.0125

    • Google Scholar
62. Report

    1. UNFPA

    (2016)

    Results of Population and Housing Census 2015 (English Version)

    Technical report, Lao Statistics Bureau.

    • Google Scholar
63. 1. Valle D

    (2014) Response to the critique by Hahn and others entitled "Conservation and malaria in the Brazilian Amazon"

    The American Journal of Tropical Medicine and Hygiene 90:595–596.

    https://doi.org/10.4269/ajtmh.14-0046

    • PubMed
    • Google Scholar
64. 1. Valle D
    2. Clark J

    (2013) Conservation efforts may increase malaria burden in the brazilian Amazon

    PLOS ONE 8:e57519.

    https://doi.org/10.1371/journal.pone.0057519

    • PubMed
    • Google Scholar
65. 1. van Etten J

    (2017) R package gdistance: distances and routes on geographical grids

    Journal of Statistical Software, Articles 76:i13.

    https://doi.org/10.18637/jss.v076.i13

    • Google Scholar
66. 1. Vittor AY
    2. Gilman RH
    3. Tielsch J
    4. Glass G
    5. Shields T
    6. Lozano WS
    7. Pinedo-Cancino V
    8. Patz JA

    (2006) The effect of deforestation on the human-biting rate of anopheles darlingi, the primary vector of falciparum malaria in the peruvian Amazon

    The American Journal of Tropical Medicine and Hygiene 74:3–11.

    https://doi.org/10.4269/ajtmh.2006.74.3

    • PubMed
    • Google Scholar
67. 1. Vittor AY
    2. Pan W
    3. Gilman RH
    4. Tielsch J
    5. Glass G
    6. Shields T
    7. Sánchez-Lozano W
    8. Pinedo VV
    9. Salas-Cobos E
    10. Flores S
    11. Patz JA

    (2009)

    Linking deforestation to malaria in the Amazon: characterization of the breeding habitat of the principal malaria vector, anopheles darlingi

    The American Journal of Tropical Medicine and Hygiene 81:5–12.

    • PubMed
    • Google Scholar
68. Website

    1. Wan Z
    2. Hook S
    3. Hulley G

    (2015) Mod11c3 modis/terra land surface temperature/emissivity monthly l3 global 0.05deg cmg v006 [data set]. nasa eosdis land processes daac

    Accessed January 15, 2020.

    https://lpdaac.usgs.gov/products/mod11c3v006/
69. 1. Wayant NM
    2. Maldonado D
    3. Rojas de Arias A
    4. Cousiño B
    5. Goodin DG

    (2010) Correlation between normalized difference vegetation index and malaria in a subtropical rain forest undergoing rapid anthropogenic alteration

    Geospatial Health 4:179–190.

    https://doi.org/10.4081/gh.2010.199

    • PubMed
    • Google Scholar
70. 1. Wen S
    2. Harvard KE
    3. Gueye CS
    4. Canavati SE
    5. Chancellor A
    6. Ahmed BN
    7. Leaburi J
    8. Lek D
    9. Namgay R
    10. Surya A
    11. Thakur GD
    12. Whittaker MA
    13. Gosling RD

    (2016) Targeting populations at higher risk for malaria: a survey of national malaria elimination programmes in the asia pacific

    Malaria Journal 15:271.

    https://doi.org/10.1186/s12936-016-1319-1

    • PubMed
    • Google Scholar
71. Report

    1. WHO

    (2016)

    Approaches for Mobile and Migrant Populations in the Context of Malaria Multi-Drug Resistance and Malaria Elimination in the Greater Mekong Subregion

    Regional Office for South-East Asia.

    • Google Scholar
72. Report

    1. WHO

    (2018)

    Geneva: World Health Organization, Technical Report, WHO

    World Report Malaria.

    • Google Scholar
73. Report

    1. Wood SN

    (2017)

    Generalized Additive Models: An Introduction with R

    Chapman and Hall/CRC.

    • Google Scholar
74. Report

    1. World Health Organization

    (2016)

    Eliminating Malaria in the Greater Mekong Subregion: United to End a Deadly Disease

    Technical report, World Health Organization.

    • Google Scholar

Author details

1. Francois Rerolle

   1. Malaria Elimination Initiative, The Global Health Group, University of California, San Francisco, San Francisco, United States
   2. Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, United States

   Contribution

   Conceptualization, Data curation, Formal analysis, Investigation, Visualization, Methodology, Writing - original draft, Writing - review and editing

   For correspondence

   francois.rerolle@ucsf.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-3837-5700

2. Emily Dantzer

   Malaria Elimination Initiative, The Global Health Group, University of California, San Francisco, San Francisco, United States

   Contribution

   Data curation, Project administration, Writing - review and editing

   Competing interests

   No competing interests declared

3. Andrew A Lover

   Department of Biostatistics and Epidemiology, School of Public Health and Health Sciences, University of Massachusetts Amherst, Amherst, United States

   Contribution

   Data curation, Funding acquisition, Project administration, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-2181-3559

4. John M Marshall

   Division of Epidemiology and Biostatistics, School of Public Health, University of California, Berkeley, Berkeley, United States

   Contribution

   Supervision, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-0603-7341

5. Bouasy Hongvanthong

   Center for Malariology, Parasitology and Entomology, Ministry of Health, Vientiane, Lao People's Democratic Republic

   Contribution

   Project administration, Writing - review and editing

   Competing interests

   No competing interests declared

6. Hugh JW Sturrock

   1. Malaria Elimination Initiative, The Global Health Group, University of California, San Francisco, San Francisco, United States
   2. Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, United States

   Contribution

   Supervision, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

7. Adam Bennett

   1. Malaria Elimination Initiative, The Global Health Group, University of California, San Francisco, San Francisco, United States
   2. Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, United States

   Contribution

   Conceptualization, Supervision, Funding acquisition, Methodology, Project administration, Writing - review and editing

   Competing interests

   No competing interests declared

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