WEBVTT FILE 1 00:00:00.000 --> 00:00:03.810 In the Amazon Rainforest, few animals are as dangerous to humans 2 00:00:03.830 --> 00:00:06.040 as mosquitoes that transmit malaria. 3 00:00:06.060 --> 00:00:11.790 In the Western Hemisphere, 90 percent of all malaria cases occur in the Amazon, 4 00:00:11.810 --> 00:00:15.080 but it’s not spread evenly across the tropical region. 5 00:00:15.100 --> 00:00:18.000 While malaria has decreased in the Brazilian Amazon, 6 00:00:18.020 --> 00:00:22.230 the disease has been steadily increasing in the Peruvian Amazon. 7 00:00:22.250 --> 00:00:27.980 In the past 5 years, Peru has had on average the second highest rate in the South American continent. 8 00:00:28.000 --> 00:00:32.340 Despite having interventions such as bed nets and indoor sprays, 9 00:00:32.360 --> 00:00:38.980 challenges still lie in identifying where to send resources before malaria outbreaks occur and spread quickly. 10 00:00:39.000 --> 00:00:43.090 Now scientists are attempting to tackle this challenge using NASA satellites. 11 00:00:43.110 --> 00:00:49.860 Our project in the Amazon is trying to understand the way malaria is transmitted in a tropical environment. 12 00:00:49.880 --> 00:00:53.160 We know that malaria risk is associated with certain environmental conditions 13 00:00:53.180 --> 00:00:54.890 that we can detect with satellites. 14 00:00:54.910 --> 00:00:57.700 To understand what environmental conditions to look for, 15 00:00:57.720 --> 00:01:00.680 it helps to know how malaria spreads in the Amazon. 16 00:01:00.700 --> 00:01:03.870 Malaria is caused by a parasite called Plasmodium 17 00:01:03.890 --> 00:01:09.230 and it’s transmitted to humans when mosquitoes carrying the parasite feed on your blood. 18 00:01:09.250 --> 00:01:14.180 There are roughly 40 species of malaria-transmitting mosquitoes worldwide, 19 00:01:14.200 --> 00:01:20.120 but in the Amazon the Anopheles darlingi species is most responsible for spreading malaria. 20 00:01:20.140 --> 00:01:25.760 The key to this study is predicting where the darlingi mosquito breeding sites are with NASA satellites. 21 00:01:25.780 --> 00:01:30.610 The project is using a model called the Land Data Assimilation System, or LDAS, 22 00:01:30.630 --> 00:01:36.980 and this gets input from NASA satellites that provide information on precipitation, temperature, 23 00:01:37.000 --> 00:01:41.920 and land cover. This informs scientists where mosquito breeding sites are likely to form. 24 00:01:41.940 --> 00:01:46.230 Mosquitoes need rainfall to form their breeding sites - puddles and ephemeral ponds. 25 00:01:46.250 --> 00:01:50.710 Rainfall also influences soil moisture. That will be important for vegetation. 26 00:01:50.730 --> 00:01:55.170 It will also change the humidity conditions near the surface where mosquitoes are breeding and living. 27 00:01:55.190 --> 00:01:57.710 Rainfall also eventually makes its way into the river 28 00:01:57.730 --> 00:02:03.130 and a lot of river discharge will mean that there’s lots of breeding sites along the banks for mosquitoes. 29 00:02:03.150 --> 00:02:08.210 There are strong patterns of malaria throughout the year, but these patterns aren’t consistent. 30 00:02:08.230 --> 00:02:10.300 As well as changes from season to season, 31 00:02:10.320 --> 00:02:16.110 global effects such as El Nino and climate change can disrupt where mosquitoes breed. 32 00:02:16.130 --> 00:02:20.010 Another factor that NASA satellites can detect is changes in land. 33 00:02:20.030 --> 00:02:26.870 The conversion of forest to non-forest is the most important change that we worry about for malaria control and the detection of hot spots. 34 00:02:26.890 --> 00:02:31.990 In Peru, land is cleared for activities such as agriculture, logging, and mining. 35 00:02:32.010 --> 00:02:35.030 And studies have found that cleared land in this region 36 00:02:35.050 --> 00:02:38.830 region increases the number of malaria-transmitting mosquitoes. 37 00:02:38.850 --> 00:02:42.540 Changes in the land doesn’t just influence where mosquitoes are, 38 00:02:42.560 --> 00:02:47.530 it also influences where humans are and this is a key component in this study. 39 00:02:47.550 --> 00:02:54.140 One thing that we’ve learnt in this project is just how important it is to consider human movement when thinking about malaria risk. 40 00:02:54.160 --> 00:02:58.490 People work on land that is used for agriculture, logging, and mining 41 00:02:58.510 --> 00:03:02.340 so that increases the amount of human traffic to that area. 42 00:03:02.360 --> 00:03:07.080 Where people meet malarial mosquitoes that’s where you get high risk of a transmission. 43 00:03:07.100 --> 00:03:11.690 Figuring out where people are getting infected forms the crux of predicting malaria. 44 00:03:11.710 --> 00:03:16.730 Peru currently measures how many malaria cases are being detected in health posts. 45 00:03:16.750 --> 00:03:20.170 But this isn’t always where true malaria outbreaks are occurring. 46 00:03:20.190 --> 00:03:24.540 People are not necessarily being diagnosed where they were infected with malaria. 47 00:03:24.560 --> 00:03:28.830 They could be getting infected where they are working, which could be 100 miles away. 48 00:03:28.850 --> 00:03:33.290 To incorporate this factor, the study is combining the LDAS data with models that 49 00:03:33.310 --> 00:03:38.070 give estimates about where people are traveling based on studies of seasonal employment. 50 00:03:38.090 --> 00:03:43.060 The study will predict where malaria outbreaks will occur 12 weeks ahead of time 51 00:03:43.080 --> 00:03:47.000 and help the country send resources to specific regions efficiently. 52 00:03:47.020 --> 00:03:49.160 While the project is focused on malaria, 53 00:03:49.180 --> 00:03:53.380 scientists say it can adapt to other diseases such as Zika and Leishmania. 54 00:03:53.400 --> 00:03:59.190 Precipitation and other environmental conditions are key factors in how diseases spread 55 00:03:59.210 --> 00:04:02.890 and with NASA satellites scientists are better understanding how 56 00:04:02.910 --> 00:04:06.050 diseases are interacting with a changing planet. 57 00:04:06.070 --> 00:04:17.637