Faculty Mentor: Dr. Zhisheng Shuai Department of Mathematics
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Cholera in Haiti: A Map to the Cure Oluwakemi Adeagbo, Sophia Morrell, Coral Nimz, Niabelle Thelemaque, Owen Thomas Faculty Mentor: Dr. Zhisheng Shuai Department of Mathematics Introduction Next Steps Methodology References Solutions Visualizations of Data Vibrio cholerae is an infectious disease of the small intestine that, if left untreated, can be deadly within a few hours [1]. Cholera was first seen in the Ganges Delta in 1817, and has been present on every populated continent since. In third world countries, cholera is a prevalent concern due to poor sanitation and insufficient resources [2]. This is especially evident in Haiti, a small country located on the Caribbean island of His- paniola, bordering the Dominican Republic. In January 2010, Haiti experienced a devastating magnitude 7 earthquake. In mid-October of that year, the first outbreak of cholera was reported, and the disease has persisted since then. In October 2016, Hurricane Matthew hit the southwestern part of Haiti near Les Anglais, refueling the epidemic. Due to lack of funds and resources, cholera has proven to be an ongoing con- cern in Haiti, and extant plans have had little long term efficacy [4]. In 1854, during a cholera epidemic in London, English physician John Snow revolutionized epidemiology by hand-mapping the locations of cholera cases (pictured below). From his map, the source of the disease was found and eventually eradicated [2]. Inspired by Snow’s work, our group will create an interactive map that will include several variables we predict are related to the spread of cholera, and thus could lead to potential solu- tions. We decided to focus specifically on Department Nord due to its climate and its distribution of rural and urban areas. Since Nord is home to one of Haiti’s largest cities, Cap-Haitien, there are more sources from which to obtain medical data. In addition, Nord has a substantial rural population, suggesting significant need for aid. We are using Excel 3D Maps in order to visually represent the re- lationship between cholera incidences and the following fac- tors: climate, medical availability, waste disposal, and clean water supply. Our data comes from various sources, including the Haitian Ministry of Public Health, weather archives, and the World Health Organization. Acknowledgments - PIC Math is a program of the Mathematical Association of America (MAA) and the Society for Industrial and Applied Mathematics (SIAM). Support is provided by the National Sci- ence Foundation (NSF grant DMS-1345499). - Special thanks to Dr. Mentor Lucien and Dr. Isaac Fung for providing data and research assistance. - Special thanks to Tina and Carl Kadolph for providing per- sonal experiences of their missionary efforts in Haiti. Citations [1] Anker, M., & Schaaf, D. (2000). World Health Organization Department of Communi cable Disease Surveillance and Response. WHO Report on Global Surveillance of Epi demic-prone Infectious Diseases, 39-57. Retrieved February 16, 2017, from http://www.who.int/csr [2] Medical Ecology. (2004). Retrieved February 02, 2017, from http://www.medicalecol ogy.org/water/cholera/cholera.htm [3] Country Historical Climate - Haiti. (2017). Retrieved March 28, 2017, from http://s dwebx.worldbank.org/climateportal/index.cfm?page=country_historical_climate& ThisCCode=HTI [4] Kadolph, Tina and Carl. Personal Communication, February 28, 2017 [5] Frwein, J. (2013, May 21). Combating Dubai’s Raw Sewage Woes. BBC. Retrieved from http://www.bbc.com/news/business-22597447 [6] Henderson, Amy K., R. Bradley Sack, and Erick Toledo. "A Comparison of Two Sys tems for Chlorinating Water in Rural Honduras." Journal of Health, Population and Nutrition (2005): 275-81. Web. We will populate our map with information about previous and current humanitarian outreach - particularly water purification systems - and pinpoint areas of highest need for future aid ef- forts. We will also perform time series analyses to search for significant trends and unexpected correlations. Our map will allow aid organizations to compare cholera inci- dences to population and climate at a regional level. Ultimately, it will also show where aid and sanitation equipment have al- ready been implemented. This will optimize the transmission of aid by facilitating decisions to either follow up with mainte- nance or begin programs in new locations. The biggest factors in reducing cholera incidences are clean water, effective sanitation, and public awareness. Disease free water can be achieved cheaply through simple at-home strain- ing and boiling, electrically-powered filter systems, or reservoir chlorinators. Effective waste disposal can be executed using ap- propriately-lined pit latrines and urban waste removal systems. Thanks to ongoing aid efforts, some of these solutions have al- ready been implemented in Haiti. Electric water filter systems using long-lasting batteries have been deployed in some villag- es throughout the country [4]. Haitian schools and villages have been provided with similarly inexpensive water chlorinators, inspired by the success of these devices in Honduras [5]. We predict that manual waste removal systems, like those in the city of Dubai, would also be successful [6]. Coupled with educa- tion and normalized hygiene practices, these solutions will greatly reduce the risk and prevalence of cholera in Haiti. (d) 1353 (h) 597 (p) 60 (b) 165 (c) 211 (l) 470 (o) 1 (n) 517 (m) 0 (e) 143 (s) 157 (k) 153 (i) 1 (q) 1 (f) 171 (a) 5 (r) 13 (g) 28 (a) Dispensaire de Bahon (b) CAL de Borgne (c) Petit Bourg Borgne (d) Hôpital Justinien (e) CSL de Dondon (f) CAL de Grande Rivière du Nord (g) CSL La Victoire (h) CAL Saint Jean de Limbé (i) Disp de Bord de Mer (j) Hôpital Sacré Coeur de Milot (k) Hôpital Bienfaisance de Pignon (l) CAL de Pilate (m) Dispensaire de Robillard (n) CM Populorum Progression (o) Dispensaire de Gobert (p) Centre Wesleyen de Port Margot (q) Hôpital de la Convention B (r) CSL de Ranquitte (s) CSL de Saint Raphael (j) 261 Number of Cholera Cases by Hospital This graph depicts Haiti’s average rainfall by month from 1990-2012 in centimeters. On average, the months with the most rainfall were May and October. These peaks indicate a dual rainy season, with torrential rain and flooding occurring in May and mul- tiple hurricanes and tropical storms in October [3]. This graph shows Haiti’s average temperature by month from 1990-2012 in Fahrenheit. The nation has a consistently warm cli- mate, with peak temperatures occurring between June and Septem- ber [3]. Acul du Nord 55908 Bahon 23251 Bas Limbe 20899 Borgne 66921 Cap Haitien 274404 Dondon 34604 Grande Riviere du Nord 41362 La Victoire 10541 Limbe 85302 Limonade 55145 Milot 31992 Pignon 43263 Pilate 54050 Plaine du Nord 41255 Plaisance 69583 Port Margot 49879 Quartier Morin 27359 Ranquitte 27704 St. Raphael 53755 Population of Nord by Commune Haiti This data was obtained from the 2015 report on population from the Haitian Institute of Statistics and Information. Population data is integral to our map as it allows us to determine the rural or urban status of a commune, as well as the relative severity of the reported cholera incidences. The cholera case data was obtained from a 2016 report from the Haitian Ministry of Public Health and Population. Low numbers could indicate that there were few cholera cases in that region, but could also be attributed to inappropriate reporting, misdiagnosis, or travel to better equipped medical facilities. 73.65 73.99 75.34 76.63 78.14 79.70 80.00 80.39 79.86 78.84 76.80 74.73 70.00 72.00 74.00 76.00 78.00 80.00 82.00 Jan. Feb. Mar. Apr. May Jun. Jul. Aug. Sep. Oct. Nov. Dec. 5.75 5.58 7.24 13.63 22.81 14.10 12.00 15.32 19.46 29.20 14.45 6.83 0.00 5.00 10.00 15.00 20.00 25.00 30.00 35.00 Jan. Feb. Mar. Apr. May Jun. Jul. Aug. Sep. Oct. Nov. Dec. Haiti Average Rainfall (cm) Haiti Average Temperature (°F)
Transcript of Faculty Mentor: Dr. Zhisheng Shuai Department of Mathematics
Cholera in Haiti: A Map to the CureOluwakemi Adeagbo, Sophia Morrell, Coral Nimz, Niabelle Thelemaque, Owen Thomas
Faculty Mentor: Dr. Zhisheng Shuai
Department of Mathematics
Introduction
Next Steps
Methodology
References
SolutionsVisualizations of Data
Vibrio cholerae is an infectious disease of the small intestine that, if left untreated, can be deadly within a few hours [1]. Cholera was first seen in the Ganges Delta in 1817, and has been present on every populated continent since. In third world countries, cholera is a prevalent concern due to poor sanitation and insufficient resources [2]. This is especially evident in Haiti, a small country located on the Caribbean island of His-paniola, bordering the Dominican Republic.
In January 2010, Haiti experienced a devastating magnitude 7 earthquake. In mid-October of that year, the first outbreak of cholera was reported, and the disease has persisted since then. In October 2016, Hurricane Matthew hit the southwestern part of Haiti near Les Anglais, refueling the epidemic. Due to lack of funds and resources, cholera has proven to be an ongoing con-cern in Haiti, and extant plans have had little long term efficacy [4].
In 1854, during a cholera epidemic in London, English physician John Snow revolutionized epidemiology by hand-mapping the locations of cholera cases (pictured below). From his map, the source of the disease was found and eventually eradicated [2]. Inspired by Snow’s work, our group will create an interactive map that will include several variables we predict are related to the spread of cholera, and thus could lead to potential solu-tions.
We decided to focus specifically on Department Nord due to its climate and its distribution of rural and urban areas. Since Nord is home to one of Haiti’s largest cities, Cap-Haitien, there are more sources from which to obtain medical data. In addition, Nord has a substantial rural population, suggesting significant need for aid.
We are using Excel 3D Maps in order to visually represent the re-lationship between cholera incidences and the following fac-tors: climate, medical availability, waste disposal, and clean water supply. Our data comes from various sources, including the Haitian Ministry of Public Health, weather archives, and the World Health Organization.
Acknowledgments - PIC Math is a program of the Mathematical Association of America (MAA) and the Society for Industrial and Applied Mathematics (SIAM). Support is provided by the National Sci-ence Foundation (NSF grant DMS-1345499). - Special thanks to Dr. Mentor Lucien and Dr. Isaac Fung for providing data and research assistance. - Special thanks to Tina and Carl Kadolph for providing per-sonal experiences of their missionary efforts in Haiti.
Citations[1] Anker, M., & Schaaf, D. (2000). World Health Organization Department of Communi cable Disease Surveillance and Response. WHO Report on Global Surveillance of Epi demic-prone Infectious Diseases, 39-57. Retrieved February 16, 2017, from http://www.who.int/csr[2] Medical Ecology. (2004). Retrieved February 02, 2017, from http://www.medicalecol ogy.org/water/cholera/cholera.htm[3] Country Historical Climate - Haiti. (2017). Retrieved March 28, 2017, from http://s dwebx.worldbank.org/climateportal/index.cfm?page=country_historical_climate& ThisCCode=HTI[4] Kadolph, Tina and Carl. Personal Communication, February 28, 2017[5] Frwein, J. (2013, May 21). Combating Dubai’s Raw Sewage Woes. BBC. Retrieved from http://www.bbc.com/news/business-22597447[6] Henderson, Amy K., R. Bradley Sack, and Erick Toledo. "A Comparison of Two Sys tems for Chlorinating Water in Rural Honduras." Journal of Health, Population and Nutrition (2005): 275-81. Web.
We will populate our map with information about previous and current humanitarian outreach - particularly water purification systems - and pinpoint areas of highest need for future aid ef-forts. We will also perform time series analyses to search for significant trends and unexpected correlations.
Our map will allow aid organizations to compare cholera inci-dences to population and climate at a regional level. Ultimately, it will also show where aid and sanitation equipment have al-ready been implemented. This will optimize the transmission of aid by facilitating decisions to either follow up with mainte-nance or begin programs in new locations.
The biggest factors in reducing cholera incidences are clean water, effective sanitation, and public awareness. Disease free water can be achieved cheaply through simple at-home strain-ing and boiling, electrically-powered filter systems, or reservoir chlorinators. Effective waste disposal can be executed using ap-propriately-lined pit latrines and urban waste removal systems.
Thanks to ongoing aid efforts, some of these solutions have al-ready been implemented in Haiti. Electric water filter systems using long-lasting batteries have been deployed in some villag-es throughout the country [4]. Haitian schools and villages have been provided with similarly inexpensive water chlorinators, inspired by the success of these devices in Honduras [5]. We predict that manual waste removal systems, like those in the city of Dubai, would also be successful [6]. Coupled with educa-tion and normalized hygiene practices, these solutions will greatly reduce the risk and prevalence of cholera in Haiti.
(d) 1353
(h) 597
(p) 60
(b) 165
(c) 211
(l) 470
(o) 1
(n) 517
(m) 0
(e) 143
(s) 157
(k) 153
(i) 1
(q) 1
(f) 171(a) 5
(r) 13
(g) 28
(a) Dispensaire de Bahon (b) CAL de Borgne(c) Petit Bourg Borgne(d) Hôpital Justinien(e) CSL de Dondon(f) CAL de Grande Rivière du Nord(g) CSL La Victoire(h) CAL Saint Jean de Limbé(i) Disp de Bord de Mer(j) Hôpital Sacré Coeur de Milot(k) Hôpital Bienfaisance de Pignon(l) CAL de Pilate(m) Dispensaire de Robillard(n) CM Populorum Progression(o) Dispensaire de Gobert(p) Centre Wesleyen de Port Margot(q) Hôpital de la Convention B(r) CSL de Ranquitte(s) CSL de Saint Raphael
(j) 261
Number of Cholera Cases by Hospital
This graph depicts Haiti’s average rainfall by month from 1990-2012 in centimeters. On average, the months with the most rainfall were May and October. These peaks indicate a dual rainy season, with torrential rain and flooding occurring in May and mul-tiple hurricanes and tropical storms in October [3].
This graph shows Haiti’s average temperature by month from 1990-2012 in Fahrenheit. The nation has a consistently warm cli-mate, with peak temperatures occurring between June and Septem-ber [3].
Acul du Nord55908
Bahon 23251
Bas Limbe20899
Borgne66921
Cap Haitien274404
Dondon34604
Grande Riviere du Nord41362
La Victoire10541
Limbe85302 Limonade
55145Milot31992
Pignon43263
Pilate54050
Plaine du Nord41255
Plaisance69583
Port Margot49879
Quartier Morin27359
Ranquitte27704
St. Raphael53755
Population of Nord by Commune
Haiti
This data was obtained from the 2015 report on population from the Haitian Institute of Statistics and Information. Population data is integral to our map as it allows us to determine the rural or urban status of a commune, as well as the relative severity of the reported cholera incidences.
The cholera case data was obtained from a 2016 report from the Haitian Ministry of Public Health and Population. Low numbers could indicate that there were few cholera cases in that region, but could also be attributed to inappropriate reporting, misdiagnosis, or travel to better equipped medical facilities.
73.6573.99
75.34
76.63
78.14
79.70 80.0080.39
79.86
78.84
76.80
74.73
70.00
72.00
74.00
76.00
78.00
80.00
82.00
Jan. Feb. Mar. Apr. May Jun. Jul. Aug. Sep. Oct. Nov. Dec.
5.75 5.58
7.24
13.63
22.81
14.10
12.00
15.32
19.46
29.20
14.45
6.83
0.00
5.00
10.00
15.00
20.00
25.00
30.00
35.00
Jan. Feb. Mar. Apr. May Jun. Jul. Aug. Sep. Oct. Nov. Dec.
Haiti Average Rainfall (cm)
Haiti Average Temperature (°F)
