This document discusses forecasting models for dengue outbreaks in San Juan, Puerto Rico. The team analyzed historical dengue case data along with environmental factors like temperature and humidity. They developed autoregressive integrated moving average (ARIMA) models to forecast quarterly dengue cases over a 3-year period. The best model included factors for 6 outbreak periods and an AR(15) term. It produced plausible forecasts with a mean absolute percentage error of 43.42%. Recommendations include targeted insecticide use and increased awareness during peak seasons.

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Dengue Endemic
Forecasting for San Juan
Team 2:
▪ Saurav Gupta
▪ Sasidhar Konda
▪ Ankita Paunikar
▪ Parmod Rathee
▪ Huixian Wang
1. Executive Summary:

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Dengue is the major cause of death and illness in Puerto Rico. There are around 4001 million
people infected every year in the world and worse yet, Dengue virus can be transmitted by
mosquito bites very quickly and for now, there is no effective vaccines to prevent the spread of
this disease. Therefore, it is extremely meaningful to analyze the historical data of Dengue
endemic, and use forecast the disease outbreaks in the future. The objective of our project is to
determine the relationship between environmental factors, such as temperature and humidity, and
the amount of disease cases, and to examine other intervention factors which might affect the
spread of Dengue disease. Through the process of model selection and analysis of forecasting
results, we are able to provide the public and governmental health services with relatively accurate
information by implementing our model. This paper reflects our conclusions based on our findings
of times series models and recommendations to better prepare for future Dengue endemic
outbreaks within limited resources.
2. Statement of the Problem:
Historical surveillance data is supported San Juan, Puerto Rico. The data include weekly
laboratory-confirmed and serotype-specific cases for the location. Environmental data (like
temperature and humidity) from weather stations, satellites, and climate models are also provided.2
Forecasted model will be able to answer following key points.
A. Timing of peak incidence, i.e when the highest incidence of dengue occurs during July
and October every year.

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B. Maximum Quarterly incidence, the number of dengue cases reported during the quarter
when incidence peaks, is during July 2008.
3. Background:
From the starting, as team, we had the consensus that we are going to do the forecasting. When
we’re looking for the datasets for this project we had verity of datasets to choose from. We had a
dataset to forecast the sales of the shampoo to the dataset where we should forecast the price of a
stock. We selected Dengue dataset as it is still an ongoing struggle of the state and to utilize our
analytics learning to something beyond business domain. To familiarize ourselves with the dataset
we read articles and documents which are listed in the references section. They provided us a great
deal of clarity as there were few biological variables in the dataset. We understand the concern of
US government in dengue control as they are devoting resources for the containment of these
endemic. On the official website of the Centers for Diseases Control and Prevention we found that
“Travel-associated dengue infections occur and several dengue outbreaks have been detected in
the continental United States, most dengue cases among U.S. citizens occur because of endemic
transmission in some U.S. territories, such as Puerto Rico”.3
4. Methodology:
Forecasting models are based on an infectious disease – dengue cases data collected by the Centers
for Disease Control and Prevention (CDC), which include satellite precipitation, humidity and
temperature from 1990 to 2007. Dengue cases forecasting models were developed using
autoregressive integrated moving average models and produced quarterly forecast over a 3-year
forecasting period.

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Event Description: In Puerto Rico, the 1994 water shortage most affected the agricultural sector,
with losses estimated at more than $94 million. Production was off by more than 50 percent for
vegetables like plantains, a staple in Puerto Rican cuisine whose price has doubled. Tourism did
not suffer, government officials say, but hotels, hospitals and other commercial customers spent
thousands of dollars a month on water to stay open. And health officials said water stored in open
containers everywhere bred mosquitoes and was a factor in the worst outbreak of dengue fever
since the 1960's.4 Dengue is endemic to Puerto Rico, which sees 3,000 to 9,000 cases in non-
epidemic years. The worst epidemics since 1990 saw 24,700 cases in 1994, 17,000 in 1998, and
10,508 in 2007. During the most recent epidemic in 2007, half of the cases were hospitalized, and
one third were hemorrhagic.5
Environmental data: Satellite-derived environmental data were obtained from National Oceanic
and Atmospheric Administration, which gave weekly average temperature (in Kelvin) and average
specific humidity (g/kg)
5. Results:
The Quarterly total cases we modeled using Point: 7 + Point: 18 + Point: 19 + Point: 34 + Point: 35 +
Point: 62 + AR(15) model. Although temperature and humidity are not significant regressors to
dengue quarterly cases, the several explosive outbreaks of dengue endemic in San Juan were all
occurred in seasons when both temperature and humidity are higher than normal days.
6. Conclusions and recommendations:

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Environmental data such as temperature, precipitation, humidity, and vegetation could be better used
to improve the accuracy of dengue predictions.
Based on our analysis we would give following recommendations:
 When analyzing characteristics of weather condition of peak transmission seasons, we could
apply proper insecticides to water storage areas during rain seasons, preventing mosquitoes
from accessing egg-laying habitats.
 Create awareness poll in San Juan about Dengue specifically before June and October month
since it is peak Dengue period every year
 As per the forecast for upcoming period government should set up mobile medical units.
 Create awareness about clean and hygienic environment among citizens.
APPENDIX:

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Time Series
Prediction Errors
Autocorrelation Plots
Stationary test probability
The series graph does not suggest any strong
trend or seasonality, however, there are at least
four major outbreaks corresponding to those
peaks in the plot. A plot of the predicted values
of the models and the original data is displayed.
The prediction errors appear to be random.
The autocorrelation plots of the residuals are
displayed and none of the spikes are significantly
different from zero. These plots confirm that the
model explains all the significant autocorrelation
that was in the original data.
The white noise tests indicate that there is no
significant autocorrelation in the residuals.
Residuals also pass tests for stationarity.

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Parameter Estimates
Statistics Fit
Forecast
Forecast dataset
The autoregressive lag12 and lag15 are not
significantly different from zero. Except these
two lags, all the other AR coefficients are
significant at the 10% level. This model should
not be disqualified because statistical significance
can be misleading and large p-values for
estimates are not enough to disqualify a model.
The RMSE for this model is 116.86, which is
superior than other models we tried. MAPE can be
interpreted as: the forecasts average a 43.42%
error.
The forecasts look plausible based on a visual
inspection of the historical data.
Evidence suggests that this is the best model we
found and we may accept this model since its
performance is superior than any other model we
tried.

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Developed Models
7. References
1. Official CDC website.
https://www.cdc.gov/dengue/index.html
2. Official website of National Oceanic and Atmospheric Administration.
http://dengueforecasting.noaa.gov/
3. Official CDC website.
https://www.cdc.gov/dengue/about/inpuerto.html
4. New York times article from 1995.
http://www.nytimes.com/1995/01/23/us/taps-go-dry-as-puerto-rico-copes-with-drought.html
5. World Health Organization
http://www.who.int/mediacentre/factsheets/fs117/en/
6. Puerto Rico Declares Dengue Epidemic
http://www.healthmap.org/site/diseasedaily/article/puerto-rico-declares-dengue-epidemic-101812
The screen capture above shows all models
during quarter 34, 19, 7, 18, 62, and 35, the
total cases of Dengue disease spike up, so we
added 6 interventions into the model. Also,
AR (15) is implied based on previous
analysis.