WEBVTT FILE 1 00:00:00.060 --> 00:00:04.060 VO: Decades of research and technology for better forecasting of the when, where 2 00:00:04.080 --> 00:00:08.080 and how intense a hurricane will be? But what if we could 3 00:00:08.100 --> 00:00:12.100 predict a disease outbreak in the wake of a storm? That's the question 4 00:00:12.120 --> 00:00:16.120 some researchers asked about cholera in Haiti in the aftermath of Hurricane 5 00:00:16.140 --> 00:00:20.140 Matthew. Cholera is a water-borne infectious disease that 6 00:00:20.160 --> 00:00:24.170 occurs when a person ingests food or water contaminated with the Vibrio 7 00:00:24.190 --> 00:00:28.190 bacterium. Cholera causes severe diarrhea, nausea, 8 00:00:28.210 --> 00:00:32.210 vomiting and dehydration and can lead to death if untreated. 9 00:00:32.230 --> 00:00:36.250 Researchers estimate that hundreds of thousands of cases are reported each year 10 00:00:36.270 --> 00:00:40.330 worldwide. Colwell: The bacterium is found in 11 00:00:40.350 --> 00:00:44.380 world oceans globally, especially 12 00:00:44.400 --> 00:00:48.420 in the temperate regions and in the tropics. So in the 13 00:00:48.440 --> 00:00:52.540 countries less developed with 14 00:00:52.560 --> 00:00:56.650 infrastructure that is not the equivalent, let's say, of Europe or the 15 00:00:56.670 --> 00:01:00.730 United States or Canada, then the population 16 00:01:00.750 --> 00:01:04.760 that has to rely on river water or pond water 17 00:01:04.780 --> 00:01:08.820 is at risk for cholera. VO: In addition to water insecurity, 18 00:01:08.840 --> 00:01:12.860 high seasonal temperatures followed by extreme rainfall, 19 00:01:12.880 --> 00:01:16.900 concentrated populations and a natural disaster 20 00:01:16.920 --> 00:01:21.020 are all conditions conducive to a cholera epidemic. This was 21 00:01:21.040 --> 00:01:25.060 the case for Haiti in 2010. Colwell: The data that we were able to 22 00:01:25.080 --> 00:01:29.080 pull together showed that in 2010 23 00:01:29.100 --> 00:01:33.090 it was the hottest summer in fifty years. And then as if 24 00:01:33.110 --> 00:01:37.110 that weren't enough, there was a hurricane that skirted 25 00:01:37.130 --> 00:01:41.130 the island, but it dumped the heaviest rainfall 26 00:01:41.150 --> 00:01:45.160 in fifty years. Jutla: We tried to make an algorithm 27 00:01:45.180 --> 00:01:49.180 in a cohesive form to determine the risk. And then that basically 28 00:01:49.200 --> 00:01:53.210 provided us with the first clues on the risk of outbreak of cholera 29 00:01:53.230 --> 00:01:57.220 in Haiti after this earthquake. 30 00:01:57.240 --> 00:02:01.250 Then we used the same algorithm 31 00:02:01.270 --> 00:02:05.300 with improved satellite datasets from Global 32 00:02:05.320 --> 00:02:09.370 Precipitation Measurement mission after Hurricane Matthew struck 33 00:02:09.390 --> 00:02:13.390 that region again. And we were 34 00:02:13.410 --> 00:02:17.420 able to, in real time, predict the risk of cholera infection in human 35 00:02:17.440 --> 00:02:21.450 population at least four weeks in advance. 36 00:02:21.470 --> 00:02:25.500 We did the same thing for Yemen. We knew there was a mass movement of 37 00:02:25.520 --> 00:02:29.540 human population due to civil unrest in that part of the world, and then we had 38 00:02:29.560 --> 00:02:33.610 very heavy precipitation. And then we immediately started 39 00:02:33.630 --> 00:02:37.630 monitoring conditions. And that basically converged to give us a 40 00:02:37.650 --> 00:02:37.730 risk on where and when this disease will 41 00:02:37.750 --> 00:02:41.780 risk on where and when this disease will 42 00:02:41.800 --> 00:02:45.860 lock in on human population. Colwell: I think we can predict and prevent 43 00:02:45.880 --> 00:02:49.970 and I'd like to see that happen very quickly, in the next three to 44 00:02:49.990 --> 00:02:54.150 five years, and I'd like to see the satellite system 45 00:02:54.170 --> 00:02:58.190 to be part of the regular public health tools so that 46 00:02:58.210 --> 00:03:02.230 we can do prediction as well as the 47 00:03:02.250 --> 00:03:06.250 tracking of epidemics that's done traditionally now. 48 00:03:06.270 --> 00:03:10.250 [ music ] 49 00:03:10.270 --> 00:03:11.532 [ music ]