Buendia in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science has been reviewed and recommended for admission. Accepted and approved in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science. The system is able to perform such a function with the help of R software which performs Time Series analysis calculations using the Autoregressive Moving Average Model (ARMA) to generate values based on the current state of the outbreak.
The system has four main users. The first is a doctor from the National Epidemiological Center (NEC). It can also assess the current status of a particular outbreak using several status indicators such as case mapping and graphs, which would help identify outbreak behavior in a faster and easier way. It is his duty to manage the user accounts of each registered user, and he is also responsible for managing the content of the website.
INTRODUCTION
B ACKGROUND
Each day they will proceed to the patient information desk where the 24-hour log is located. The names of patients who were admitted in the last 24 hours and whose diagnosis is included among the monitored diseases are copied. Surveillance data from each Regional Epidemiology and Surveillance Unit is sent to the NEC by post (diskettes) or email (for regions with Internet access) monthly for collection and aggregation.
The Outbreak Detection System is a web-based system designed to generate outbreak status reports based on reported cases from hospitals and other health facilities.
S TATEMENT OF THE P ROBLEM
Data comes from local health personnel in the field through regional and provincial health offices and consolidated at the central office. It also provides additional tools that can assist various health sectors in formulating actions to prevent and control disease outbreaks. Although several systems have been developed with the same goal, the system has not been able to detect the outbreak of the disease at the local level considering the local conditions.
And if there is any system already in use, it offers limited functions to evaluate disease outbreaks. Also, according to an epidemiologist, there are still discrepancies between what is indicated in FHSIS annual reports and what actually happens at the health centers in relation to data collection and recording system. 3] The variance can be explained by some missing or delayed records while they are all being consolidated in the NEC or errors due to duplication of records.
O BJECTIVE
S IGNIFICANCE OF THE S TUDY
S COPE AND L IMITATIONS
A SSUMPTIONS
REVIEW OF RELATED LITERATURE
As a result, the ARIMA model precisely fitted the previous monthly incidence rate from January. A modified ARIMA model was then used to predict HCV seropositive donors for 91–96 months, contrasting with the observed ranges of the same months. The main objective of the study is to apply autoregressive integrated moving average (ARIMA) models to make real-time predictions on the number of beds occupied at Tan Tock Seng Hospital during the recent SARS outbreak.
They found that the ARIMA (1,0,3) model was able to describe and predict the number of occupied beds during the SARS outbreak. In addition, the model also provided three-day forecasts of the number of beds required. Seasonal autoregressive integrated moving average (ARIMA) and Winters exponential smoothing models were developed and tested on datasets belonging to two sites: Telegraph Road and the Woodrow Wilson Bridge on the inner and outer loops of the Capital Beltway in northern Virginia . Continuum spectral analysis of the data was developed by Fourier transform and various Box-Jenkins autoregressive integrated moving average models were fitted.
THEORETICAL FRAMEWORK
Time series analysis includes methods that attempt to understand such time series, often to understand the underlying context of the data points (where did they come from? What produced them?), or to make predictions (forecasts). A standard example in econometrics is the opening price of a stock based on its past performance. It is widespread in tropical and subtropical regions, including parts of the Americas, Asia and Africa.
Leprosy is primarily a granulomatous disease of the peripheral nerves and the mucosa of the upper respiratory tract; skin lesions are the main external symptom. A set of computer programs that control the creation, maintenance, and use of an organization's database and its end users. In a narrow sense, the term information system (or computer-based information system) refers to the specific application software used to store data records in a computer system and automate part of the organization's information processing activities.
DESIGN AND IMPLEMENTATIONS
PROCESS SUBEXPOSITION 2: GENERATE EXPLOSION STATUS Figure 3 shows the Process 2 Generate Explosion Status subexplosion. Note that only the System Administrator is responsible for managing all user accounts within the system. On the other hand, the NEC medical officer is the only one who can view the outbreak status and also the disease records stored in the system.
While the system administrator can view the accounts of the users registered in the system. Here, the local field health staff is responsible for managing the patient record, while the NEC medical officer is responsible for maintaining the disease records. The system administrator is responsible for managing various user accounts and updating the content of the site.
RESULTS
After the previous page, the Registration Update Page shown in Figure 13 provides several functions for the user. This site is also accessible only to registered users, specifically to health personnel in the field. This page allows users to easily manage all the data that the system stores.
The page is displayed with a maximum of ten patient records per page in case of multiple results. Here, the user is given the opportunity to add a new disease record to the database. A brief summary of the trend of the outbreak for a specific time interval is shown here, along with the prediction values and standard error limits.
Here the user is given options for which tool he/she will use to assess the outbreak of the disease. This page displays the appropriate graph showing the outbreak status of a particular disease. In the figure we can see that the frequency of reported cases is shown per month, so that the behavior of the outbreak can be easily analyzed.
The Annual Report page is shown in Figure 23. This page presents the summary report of a disease for a given year. Here, the user can contribute an article related to health issues that will be displayed and viewed by all users.
DISCUSSION
The result page will display the report in a PDF format allowing the user to save a copy for archival purposes. The generated report follows the standard and format of the reports created and used by NEC. For the status graphs, the user is required to enter the specific disease to plot and the type of graph to use.
These features help assess the current status of the disease outbreak in the capital region. Finally, the case mapping feature requires the user to enter a specific disease to view. A map of the NCR will be displayed on the results page, with cities colored differently based on the level of risk for a particular disease.
High-risk cities are colored red, green for average risk levels, and cities with low-risk levels are colored yellow. The system also provides an additional function to the NEC doctor and this function is to publish Health News. This allows the NEC physician to create health related articles or news items reflecting the status of an outbreak to inform other users as online visitors of an outbreak.
Overall, disease outbreak detection system provides useful tool for health personnel to analyze and understand the behavior and trend of an outbreak. The outputs generated by the system will reflect the current or even future status of the outbreak, giving health personnel a basis in their decision-making and in planning their actions to prevent and control an outbreak. And the system also helps to provide status reports and analysis of an outbreak with less errors and in a much faster way.
CONCLUSION
RECOMMENDATION
BIBLIOGRAPHY
ACKNOWLEDGEMENT
He proved to me that he is always by my side in every obstacle I encountered and he still continues to give me the courage to push through and never give up. Who never give up on me and always remind me that no matter what, I should finish my studies because that is the most important thing they can give me. Despite the problems we faced together, they stay right by my side to get me back on track again.
I would also like to express my deepest gratitude to all my teachers for dedicating their time to sharing their wisdom with us. To my very dedicated and supportive SP advisor Sir Solano, who was so patient with me throughout my SP time. I am grateful that he gave me his free time when I sought advice on my SP and that he prepared me for my presentations.
I am very lucky that he was my advisor because I can't imagine myself under the control of others (hehehehe…). My life at UP became a very learning experience for me because of the best teachers I had. Projects, machine problems, assignments and other school matters are common topics of our conversations.
But we are not that nerdy type, except for a few, we also share some experiences that a typical person does in early adulthood. We regularly go to a certain place at Robinson and that's why I feel like the staff in charge of the machines already recognized us. I am very happy to have met this wonderful group of people with whom I share some common interests (the Irregz) and with other people (CompSci people, DevStud people and others) who made my university.
