scholarly journals A Model of Vaccination for Dengue in the Philippines 2016–2018

Author(s):  
Pierre Magal ◽  
Ousmane Seydi ◽  
Glenn Webb ◽  
Yixiang Wu

A mathematical model of the dengue epidemic in the Philippines is developed to analyse the vaccination of children in 2016–2017. Reported case data and reported mortality data from the Philippines Department of Health is used to analyze quantitatively this vaccination program. The model compares the epidemic outcomes of no vaccination of children, vaccination only of previously infected children, and vaccination of all children.

2021 ◽  
Vol 8 (6) ◽  
pp. 201965
Author(s):  
Pamela Kim N. Salonga ◽  
Victoria May P. Mendoza ◽  
Renier G. Mendoza ◽  
Vicente Y. Belizario

Despite being one of the first countries to implement mass drug administration (MDA) for elimination of lymphatic filariasis (LF) in 2001 after a pilot study in 2000, the Philippines is yet to eliminate the disease as a public health problem with 6 out of the 46 endemic provinces still implementing MDA for LF as of 2018. In this work, we propose a mathematical model of the transmission dynamics of LF in the Philippines and a control strategy for its elimination using MDA. Sensitivity analysis using the Latin hypercube sampling and partial rank correlation coefficient methods suggests that the infected human population is most sensitive to the treatment parameters. Using the available LF data in Caraga Region from the Philippine Department of Health, we estimate the treatment rates r 1 and r 2 using the least-squares parameter estimation technique. Parameter bootstrapping showed small variability in the parameter estimates. Finally, we apply optimal control theory with the objective of minimizing the infected human population and the corresponding implementation cost of MDA, using the treatment coverage γ as the control parameter. Simulation results highlight the importance of maintaining a high MDA coverage per year to effectively minimize the infected population by the year 2030.


2017 ◽  
Vol 17 (4) ◽  
pp. 1168-1177 ◽  
Author(s):  
B. B. Magtibay

Developing a water safety plan (WSP) is now a requirement for all service providers of drinking water in the Philippines. To assist compliance with the Philippine Department of Health (DOH), this study develops an index model that the DOH can use for evaluating WSPs and covers the WSPs of 14 water districts and 11 health care facilities. The WSP Index model was developed using a nine-step process and was tested in 25 WSPs to determine the robustness of its weights and benchmark. Approximately 21 WSPs received a passing mark when the 60% benchmark was used but only nine WSPs passed when the benchmark was raised to 74%. This Philippine model may be utilized by countries in evaluating the WSPs, and further adapted to their local context and considerations.


Author(s):  
W M G Malalasekera ◽  
F Lockwood

A mathematical model has been applied to simulate model experiments of the 1987 King's Cross underground fire by the Department of Health and Safety Executive. The predicted growth of the fire is compared with the experimental data and in particular the predicted and measured times to ‘flashover’ are compared. The comparisons show exceptional agreement which, in part, may be fortuitous due to the need to facilitate the prediction of the early stages of the growth with the aid of an experimentally estimated fire strength. The good agreement nonetheless is also due to the full description of the radiation transfer which is a feature of the mathematical model. It is concluded that the flashover phenomenon that occurred at King's Cross was thermal radiation driven and that future research should be devoted to modelling the details of fire spread across a combustible surface.


Biology ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 50 ◽  
Author(s):  
Zhihua Liu ◽  
Pierre Magal ◽  
Ousmane Seydi ◽  
Glenn Webb

We develop a mathematical model to provide epidemic predictions for the COVID-19 epidemic in Wuhan, China. We use reported case data up to 31 January 2020 from the Chinese Center for Disease Control and Prevention and the Wuhan Municipal Health Commission to parameterize the model. From the parameterized model, we identify the number of unreported cases. We then use the model to project the epidemic forward with varying levels of public health interventions. The model predictions emphasize the importance of major public health interventions in controlling COVID-19 epidemics.


2021 ◽  
Vol 9 (1) ◽  
pp. 262-272
Author(s):  
Randy L. Caga-anan ◽  
Michelle N. Raza ◽  
Grace Shelda G. Labrador ◽  
Ephrime B. Metillo ◽  
Pierre del Castillo ◽  
...  

Abstract A mathematical model of COVID-19 with a delay-term for the vaccinated compartment is developed. It has parameters accounting for vaccine-induced immunity delay, vaccine effectiveness, vaccination rate, and vaccine-induced immunity duration. The model parameters before vaccination are calibrated with the Philippines’ confirmed cases. Simulations show that vaccination has a significant effect in reducing future infections, with the vaccination rate being the dominant determining factor of the level of reduction. Moreover, depending on the vaccination rate and the vaccine-induced immunity duration, the system could reach a disease-free state but could not attain herd immunity. Simulations are also done to compare the effects of the various available vaccines. Results show that Pfizer-BioNTech has the most promising effect while Sinovac has the worst result relative to the others.


2018 ◽  
Vol 52 (2) ◽  
Author(s):  
Ronald P. Law

Background. Mass gatherings (MG) are events that draw together a large number of people in one or several occasions happening in single or multiple places for a definite period of time. These can lead to different public health risks through exposure to infectious diseases, trauma, and environmental factors. The Philippine Department of Health (DOH) in 2015 participated in special planned events that constituted mass gatherings namely the AsiaPacific Economic Cooperation (APEC) meetings, the Black Nazarene procession, and the Papal Visit. Objective. The study aimed to describe the different health risks arising from the three (3) identified mass gathering events in the Philippines in 2015 and relate them to public health preparedness. Methods. This was a descriptive study of the health risks arising from the MG events. Sources of data were reports submitted by deployed medical teams to the Operations Center (Opcen) that closely monitored the MG. Results. The study found infectious causes, trauma, temperature-related conditions, and noncommunicable diseases to be the important categories of health risks in the specified mass gatherings. These validated the common health risk categories observed in previously well-studied mass gatherings. Conclusion. The study highlighted important health risks and factors for consideration in public health preparedness for mass gatherings in terms of appropriate and effective public health strategies that should be established to minimize health risks and reduce health system impacts of mass gatherings.


2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Evan Mobley ◽  
Chelsea Fischer ◽  
Andrew Hunter

ObjectiveLink emergency department (ED) with death certificate mortality data in order to examine the prior medical history of opioid overdose victims leading up to their death.IntroductionIn 2017, 951 Missouri residents died from an opioid overdose—a record number for the state.1 This continues the trend from 2016, which saw an increase of over 30% in opioid overdose deaths compared to 2015. The Missouri Department of Health and Senior Services (MDHSS) manages several public health surveillance data sources that can be used to inform about the opioid epidemic. Opioid overdose deaths are identified through death certificates which are collected through the vital records system. MDHSS also manages the Patient Abstract System (PAS), which contains ED and inpatient hospitalization data from approximately 132 non-federal Missouri hospitals. PAS contains about 130 variables, which include demographic data, diagnoses codes, procedures codes, and other visit information. Records can have up to 23 diagnosis fields, which are coded using ICD-10-CM (International Classification of Diseases, Clinically Modified). The first diagnosis field is the primary reason for a visit.MethodsLinkage and analysis of the data was performed using SAS Enterprise Guide 6.1. Opioid overdose deaths were identified through ICD-10 analysis looking for drug poisoning underlying cause of death codes and opioid-specific codes found in the multiple cause (contributing cause) of death fields. Table 1, below, summarizes the ICD-10 codes used. Mortality data from the 951 decedents were linked to ED data from 2016 and 2017. Records were linked using multiple passes over the ED records. Records were first linked on social security number. Following this linkage, ED records with no initial match went through a second pass and linked on name and date of birth. Finally, a third pass for records still without a match was conducted using date of birth, census tract, and sex. After these passes, the linkages were reviewed to identify any false positives. The 23 diagnosis fields contained in PAS were analyzed to look for patterns in diagnosis coding. ICD-10-CM codes were too broad so CCS (Clinical Classifications Software) categories were utilized.ResultsIn total, 3,500 ED records were linked to the 951 decedents. After removing false positives, the total number of ED records was 3,357. Approximately 70% (687) of decedents were linked to at least one ED record. One hundred and eighty-eight visits were due to drug overdose (153 opioid overdoses). The most common primary diagnosis CCS categories (category numbers in parentheses) were: substance-related disorders (661), Spondylosis; intervertebral disc disorders; other back problems (205), abdominal pain (251), and other nervous system disorders (95). Collectively, these four categories represented over 20% of all primary diagnoses. Across all 23 diagnosis fields there were similar results. The most common CCS categories were as follows: substance-related disorders (661), other aftercare (257), essential hypertension (98), and mood disorders (657). Pie charts (Fig. 1 and 2) below show proportions of CCS categories across all diagnoses fields and primary diagnosis broken into three major categories: pain/injury, substance abuse/mental health, and other. In order to reduce the impact of CCS categories with small numbers, these graphics represent only CCS categories that made up 1% or more of the total collection of diagnoses codes. Of the 687 decedents that were matched successfully to ED records, 96% had at least one pain/injury or one substance abuse/mental health ICD-CM code in at least one record, and 68% had both.ConclusionsThese findings suggest that many overdose decedents visited the ED in the years prior to death. Many of these visits were not due to an overdose; however, they could be indicative of a problem with opioids (i.e. pain, drug-seeking, substance use-related). ED staff and public health professionals could utilize these opportunities to refer patients to recovery services and recommend they heed caution when using opioids.References1. Missouri Department of Health and Senior Services. (2018). Missouri Resident Overdose Deaths by Opioid Type. Retrieved September 27, 2018 from https://health.mo.gov/data/opioids/pdf/opioid-dashboard-slide-9.pdf.


2022 ◽  
Vol 4 (1) ◽  
pp. 118-130
Author(s):  
Paul Benjamin Barrion ◽  
Ray Patrick Basco ◽  
Kevin jamir Pigao

In the heightened effects of the pandemic, health resources have been in constant limbo as supplies and availability of hospital resources take a toll as COVID-19 cases surge, resulting in shortages. Thus, health systems are overwhelmed, resulting in a higher fatality rate since the capacity to provide medical attention is diminished. In this paper, hospital resources refer to mechanical ventilators, ICU, isolation, and ward beds which are the critical factors of the case fatality rate (CFR) of COVID-19 in the Philippines. Data were retrieved from the Department of Health (DOH) Case Bulletins from October 26, 2020, to June 30, 2021, with 248 total observations. This research used the Ordinary Least Squares (OLS) Multiple Regression to determine if hospital resources are the predictors of the case fatality rate of COVID-19. Furthermore, the results show a significant relationship between the hospital resources and the case fatality rate of COVID-19 in the Philippines. This study can become a framework for further research concerned about hospital resources as the predictors of case fatality rates of different diseases in a pandemic.  


Author(s):  
Christian Alvin H. Buhat ◽  
Monica C. Torres ◽  
Yancee H. Olave ◽  
Maica Krizna A. Gavina ◽  
Edd Francis O. Felix ◽  
...  

ABSTRACTThe number of COVID-19 cases is continuously increasing in different countries (as of March 2020) including the Philippines. It is estimated that the basic reproductive number of COVID-19 is around 1.5 to 4. The basic reproductive number characterizes the average number of persons that a primary case can directly infect in a population full of susceptible individuals. However, there can be superspreaders that can infect more than this estimated basic reproductive number. In this study, we formulate a conceptual mathematical model on the transmission dynamics of COVID-19 between the frontliners and the general public. We assume that the general public has a reproductive number between 1.5 to 4, and frontliners (e.g. healthcare workers, customer service and retail personnel, food service crews, and transport or delivery workers) have a higher reproduction number. Our simulations show that both the frontliners and the general public should be protected or resilient against the disease. Protecting only the frontliners will not result in flattening the epidemic curve. Protecting only the general public may flatten the epidemic curve but the infection risk faced by the frontliners is still high, which may eventually affect their work. Our simple model does not consider all factors involved in COVID-19 transmission in a community, but the insights from our model results remind us of the importance of community effort in controlling the transmission of the disease. All in all, the take-home message is that everyone in the community, whether a frontliner or not, should be protected or should implement preventive measures to avoid being infected.


Sign in / Sign up

Export Citation Format

Share Document