1b:I[42426,["/_next/static/immutable/chunks/1xctgzqre-wsy.js","/_next/static/immutable/chunks/06_-wz6pdtp2b.js","/_next/static/immutable/chunks/30e52g49hg40p.js","/_next/static/immutable/chunks/02yq6tul2z-oh.js","/_next/static/immutable/chunks/10bx_ec2vj7n_.js","/_next/static/immutable/chunks/33bfn03fo8xnt.js","/_next/static/immutable/chunks/0tfjf78scu61b.js","/_next/static/immutable/chunks/31xnvej4n1bi9.js"],"default"] 1c:T5b2,

Data relating cholera and rainfall from four areas in Haiti:

Country-wide cholera case data from Haiti Ministry of Health (MSPP), together with rainfall data from NASA Tropical Measurement Mission.
Cholera case data from Hospital Albert Schweitzer (Haiti), together with rainfall data from NASA Tropical Measurement Mission.
Cholera case data from Port-au-Prince, as reported by the MSPP, together with rainfall data measured from USGS rain gauge in Foret de Pins.
Cholera case data from Internally Displaced Persons camps, together with rainfall data from USGS rain gauge in Morne Gentilehomme.

These data were reported in:
Examining rainfall and cholera dynamics in Haiti using statistical and dynamic modeling approaches.
Eisenberg MC1, Kujbida G, Tuite AR, Fisman DN, Tien JH.
Epidemics. 2013 Dec;5(4):197-207.

http://www.sciencedirect.com/science/article/pii/S1755436513000431
doi:10.1016/j.epidem.2013.09.004

DMO notes:
Rainfall was made into two columns, one for each unit of measure.
The four received spreadsheets were into one dataset.

1d:Tb40,Funding was requested to analyze and model cholera epidemics. In order to accomplish this task, the team will analyze spatio-temporal data from cholera outbreaks in Haiti (2010), Angola (2006) and London (1832); develop mathematical theory for coupled patch models of waterborne disease; and develop models to predict disease spread under different scenarios. The study will explore the contribution of humans (direct) and of environmental (delayed) pathways to disease transmission and investigate how heterogeneity in water quality, which is ubiquitous in developing countries, affects cholera disease dynamics. This project will advance understanding of the factors governing the spatial spread of cholera, examine how the arrangement and connectivity between cholera risk hot spots influence disease spread and develop a modeling framework for rapid response to a cholera crisis. The proposed study has clear practical and theoretical significance for understanding and predicting not only cholera transmission, but other waterborne diseases as well, reaching beyond the specific study system. The team will develop mathematical theory for coupled patch models of waterborne diseases and advance understanding of the effects of movement of individuals and of water to cholera transmission. The team will also explore inclusion of data from early stages of an outbreak on parameter estimates and whether information on water quality and availability and on types of sanitation facilities available can be used to improve knowledge of model parameters before the disease has reached a given area. This study will result in the training of undergraduates and graduate students, and include outreach to the Columbus Science Pub (public), Cornell's Summer Math Institute (undergraduates) and the UCLA Math Circle (grades K-12). This project will strengthen international scientific collaboration through interaction with scientists and students from the University of Toronto. The research from this study will provide society benefits through improved understanding of the factors influencing the ability of cholera to invade, spread and persist in a region. The proposed study will provide a tool to predict disease spread under different scenarios for rapid response to a cholera crisis and evaluate the efficacy of different intervention strategies on containing cholera spatial spread. The team will interact and communicate their research findings to the Centers for Disease Control and the National Biosurveillance Integration Center within the Department of Homeland Security. They will collaborate with the United Nations University Institute for Water, Environment and Health to compile a database of time series data from cholera outbreaks worldwide, in association with data on water quality, water availability, and sanitation facilities from several outbreak locales.6:["$","$L1b",null,{"dataset":{"_id":"644730","description":"$1c","problem":"","acquisition":"

Aggregated weekly cholera cases were collected at three levels of granularity: country, city, and internally displaced persons camps (IDP). Rainfall was collected from USGS rain gauges at Morne Gentilehomme, and Foret de Pins. Rainfall was also collected from NASA satellite data over two areas: lat=[18.041N,19.844N] and lon=[74.323W,71.79W], lat=[19.0N,19.2N] and lon=[72.6W,72.4W].

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start date of weekly data collection

","units":{"_id":"","name":"yyyymmdd"},"type":{"_id":"502628","name":"date_start","description":"

date sampling starts such as YYYYMMDD

"}},{"_id":"644739","name":"days_since_start","format":"","frictionlessType":"","description":"

number of days since date_start

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amount of rain per week from NASA satellite data

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precipitation level in gauge. Can be in inches or millimeters.

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amount of rain per week from USGS rain gauges

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precipitation level in gauge. Can be in inches or millimeters.

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number of cholera cases per week

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An association with a community-wide standard parameter has not yet been made.

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