Infodemiology for Syndromic Surveillance of Dengue and Typhoid Fever in the Philippines

Finding determinants of disease outbreaks before its occurrence is necessary in reducing its impact in populations. The supposed advantage of obtaining information brought by automated systems fall short because of the inability to access real-time data as well as interoperate fragmented systems, leading to longer transfer and processing of data. As such, this study presents the use of realtime latent data from social media, particularly from Twitter, to complement existing disease surveillance efforts. By being able to classify infodemiological (health-related) tweets, this study is able to produce a range of possible disease incidences of Dengue and Typhoid Fever within the Western Visayas region in the Philippines. Both diseases showed a strong positive correlation (R \u3e .70) between the number of tweets and surveillance data based on official records of the Philippine Health Agency. Regression equations were derived to determine a numerical range of possible disease incidences given certain number of tweets. As an example, the study shows that 10 infodemiological tweets represent the presence of 19-25 Dengue Fever incidences at the provincial level

Mathematical Analysis of a COVID-19 Compartmental Model with Interventions

Mathematical models of the COVID-19 pandemic have been utilized in a variety of settings as a core component of national public health responses. Often based on systems of ordinary differential equations; compartmental models are commonly used to understand and forecast outbreak trajectories. In view of the primarily applied nature of COVID-19 models; theoretical analysis can provide a global and long-term perspective of key model properties; and relevant insights about the infection dynamics they represent. This work formulates and undertakes such an investigation for a compartmental model of COVID-19; which includes the effect of interventions. More specifically; this paper analyzes the characteristics of the solutions of a compartmental model by establishing the existence and stability of the equilibrium points based on the value of the basic reproductive number R0. Our results provide insights on the possible policies that can be implemented to address the health crisis

Towards an Infodemiological Algorithm for Classification of Filipino Health Tweets

Finding innovative ICT solutions to enhance the Philippines’ health sector is part and parcel of the Philippine eHealth Strategic Framework and Plan 2020 program. This study sees the opportunity of using collected Twitter data to create a model that processes tweets to produce a dataset that may be relevant in the field of epidemiology and infodemiology. Through the collection of relevant tweets, future studies may make use of the output of this research for various purposes, such as the improvement of epidemiological systems of the Department of Health in support of the eHealth strategy. In this study, we used the Naïve-Bayes classification model, an efficient text classifier, to create a model that determines whether a tweet is “infodemiological” or not. From the collected 18,044 tweets, we have narrowed it down to 1,090 tweets (6.04%) that can be used in epidemiology. Using this as a dataset for training and testing, the model was able to classify 79.91% of tweets correctly. This research shows that it is indeed feasible to collect and classify enough infodemiological tweets in the Filipino language, which in turn can be used for future infodemiological studies

Effects of hospital facilities on patient outcomes after cancer surgery: an international, prospective, observational study

Background Early death after cancer surgery is higher in low-income and middle-income countries (LMICs) compared with in high-income countries, yet the impact of facility characteristics on early postoperative outcomes is unknown. The aim of this study was to examine the association between hospital infrastructure, resource availability, and processes on early outcomes after cancer surgery worldwide.Methods A multimethods analysis was performed as part of the GlobalSurg 3 study-a multicentre, international, prospective cohort study of patients who had surgery for breast, colorectal, or gastric cancer. The primary outcomes were 30-day mortality and 30-day major complication rates. Potentially beneficial hospital facilities were identified by variable selection to select those associated with 30-day mortality. Adjusted outcomes were determined using generalised estimating equations to account for patient characteristics and country-income group, with population stratification by hospital.Findings Between April 1, 2018, and April 23, 2019, facility-level data were collected for 9685 patients across 238 hospitals in 66 countries (91 hospitals in 20 high-income countries; 57 hospitals in 19 upper-middle-income countries; and 90 hospitals in 27 low-income to lower-middle-income countries). The availability of five hospital facilities was inversely associated with mortality: ultrasound, CT scanner, critical care unit, opioid analgesia, and oncologist. After adjustment for case-mix and country income group, hospitals with three or fewer of these facilities (62 hospitals, 1294 patients) had higher mortality compared with those with four or five (adjusted odds ratio [OR] 3.85 [95% CI 2.58-5.75]; p<0.0001), with excess mortality predominantly explained by a limited capacity to rescue following the development of major complications (63.0% vs 82.7%; OR 0.35 [0.23-0.53]; p<0.0001). Across LMICs, improvements in hospital facilities would prevent one to three deaths for every 100 patients undergoing surgery for cancer.Interpretation Hospitals with higher levels of infrastructure and resources have better outcomes after cancer surgery, independent of country income. Without urgent strengthening of hospital infrastructure and resources, the reductions in cancer-associated mortality associated with improved access will not be realised

Evaluation of appendicitis risk prediction models in adults with suspected appendicitis

Background Appendicitis is the most common general surgical emergency worldwide, but its diagnosis remains challenging. The aim of this study was to determine whether existing risk prediction models can reliably identify patients presenting to hospital in the UK with acute right iliac fossa (RIF) pain who are at low risk of appendicitis. Methods A systematic search was completed to identify all existing appendicitis risk prediction models. Models were validated using UK data from an international prospective cohort study that captured consecutive patients aged 16–45 years presenting to hospital with acute RIF in March to June 2017. The main outcome was best achievable model specificity (proportion of patients who did not have appendicitis correctly classified as low risk) whilst maintaining a failure rate below 5 per cent (proportion of patients identified as low risk who actually had appendicitis). Results Some 5345 patients across 154 UK hospitals were identified, of which two‐thirds (3613 of 5345, 67·6 per cent) were women. Women were more than twice as likely to undergo surgery with removal of a histologically normal appendix (272 of 964, 28·2 per cent) than men (120 of 993, 12·1 per cent) (relative risk 2·33, 95 per cent c.i. 1·92 to 2·84; P < 0·001). Of 15 validated risk prediction models, the Adult Appendicitis Score performed best (cut‐off score 8 or less, specificity 63·1 per cent, failure rate 3·7 per cent). The Appendicitis Inflammatory Response Score performed best for men (cut‐off score 2 or less, specificity 24·7 per cent, failure rate 2·4 per cent). Conclusion Women in the UK had a disproportionate risk of admission without surgical intervention and had high rates of normal appendicectomy. Risk prediction models to support shared decision‐making by identifying adults in the UK at low risk of appendicitis were identified

Estimating parameters for a dynamical dengue model using genetic algorithms

Dynamical models are a mathematical framework for understanding the spread of a disease using various epidemiological parameters. However, in data-scarce regions like the Philippines, local estimates of epidemiological parameters are difficult to obtain because methods to obtain these values are costly or inaccessible. In this paper, we employ genetic algorithms trained with novel fitness functions as a low-cost, data-driven method to estimate parameters for dengue incidence in the Western Visayas Region of the Philippines (2011-2016). Initial results show good ht between monthly historical values and model outputs using parameter estimates, with a best Pearson correlation of 0.86 and normalized error of 0.65 over the selected 72-month period. Furthermore, we demonstrate a quality assessment procedure for selecting biologically feasible and numerically stable parameter estimates. Implications of our findings are discussed in both epidemiological and computational contexts, highlighting their application in FASSSTER, an integrated syndromic surveillance system for infectious diseases in the Philippines

Health Emergency and Public Involvement in the Philippines: Syndromic Surveillance Efforts and System Integration

In a country situated within the typhoon belt, the Philippines experience an average of 28 typhoons in a year with a minimum of two major typhoons causing great damage and loss of lives. Acknowledging that disaster preparedness, response, and rescue require a multidimensional approach, emergency clusters recognize the need for an ICT platform that will consolidate disaster information coming from various sources including the public. The Philippine government, through the Department of Health (DOH) and the Philippine Health Insurance Company (PHIC), has mandated all rural health units to select and use an electronic medical record (EMR) system for digitizing health records. The digitization of health records in primary care is the first step in achieving universal health care for the country at the same time providing a more efficient way of accessing health records during emergencies. This chapter discusses efforts and experiences in the development and deployment of eHealth systems at three levels: (1) digitizing health records (SHINE OS+), (2) syndromic surveillance (FASSSTER), and (3) seamless integration with a disaster management system (HDDX), all of which provide a preventive approach in health emergencies and extreme disasters. SHINE OS+ is a web- and mobile-based electronic medical and referral system that is used by rural health units to digitize and submit health data for Primary Care Benefit (PCB) program and eClaims. FASSSTER is an online syndromic surveillance tool that collects information from electronic medical records, other eHealth systems, and social media to develop a geospatial disease forecast model. However, the lack of a unified disaster management system that provides health-related information motivated the development of HDX (Health Disaster Exchange) comprising of API endpoints to integrate health information in disaster management systems. The chapter ends with perspectives on use of ICT for health emergency and disaster risk management practices and how the public provides both personal and social contributions to zero casualty

Integrating health indices towards the development of a Typhoid disease model using STEM

Typhoid Fever is a health concern that should not be overlooked, especially after natural disasters. Even with vaccinations and antibiotics available, the number of incidence remains high. Considered to be one of the communicable food- and water-borne diseases that can spread especially after a natural disaster, Typhoid Fever is a disease that imposes health threats to the community proven by its constant appearance in the top ten leading causes of morbidity in different Philippine regions. Modeling and analyzing the spatio-temporal epidemiological behavior of Typhoid Fever can aid in its early detection and possibly prevent outbreaks. This paper aims to identify disease parameters that lead to suspected and identified spread of Typhoid Fever in Region VI (Western Visayas) Philippines by mining pertinent data from a government-based project called PIDSR and an electronic medical record (EMR) system, SHINE OS+ Moreover, this paper aims to utilize the modeling tools of Spatio-Temporal Epidemiological Modeler (STEM) to provide a new and localized disease model for Typhoid Fever using appropriate local disease parameters to form differential equations. Sensitivity analysis showed three parameters that greatly contribute to the outcome of the simulation: transmissionRate, transmissionRateC, and recovery Rate. Further statistical analysis showed an average of 61% correlation coefficient and 1.34 % normalized mean squared error between the simulated result and the actual data from PIDSR

Policy-Driven Mathematical Modeling for COVID-19 Pandemic Response in the Philippines

Around the world; disease surveillance and mathematical modeling have been vital tools for government responses to the COVID-19 pandemic. In the face of a volatile crisis; modeling efforts have had to evolve over time in proposing policies for pandemic interventions. In this paper; we document how mathematical modeling contributed to guiding the trajectory of pandemic policies in the Philippines. We present the mathematical specifications of the FASSSTER COVID-19 compartmental model at the core of the FASSSTER platform; the scenario-based disease modeling and analytics toolkit used in the Philippines. We trace how evolving epidemiological analysis at the national; regional; and provincial levels guided government actions; and conversely; how emergent policy questions prompted subsequent model development and analysis. At various stages of the pandemic; simulated outputs of the FASSSTER model strongly correlated with empirically observed case trajectories (–; ). Model simulations were subsequently utilized to predict the outcomes of proposed interventions; including the calibration of community quarantine levels alongside improvements to healthcare system capacity. This study shows how the FASSSTER model enabled the implementation of a phased approach toward gradually expanding economic activity while limiting the spread of COVID-19. This work points to the importance of locally contextualized; flexible; and responsive mathematical modeling; as applied to pandemic intelligence and for data-driven policy-making in general

Policy-driven mathematical modeling for COVID-19 pandemic response in the Philippines

Around the world, disease surveillance and mathematical modeling have been vital tools for government responses to the COVID-19 pandemic. In the face of a volatile crisis, modeling efforts have had to evolve over time in proposing policies for pandemic interventions. In this paper, we document how mathematical modeling contributed to guiding the trajectory of pandemic policies in the Philippines. We present the mathematical specifications of the FASSSTER COVID-19 compartmental model at the core of the FASSSTER platform, the scenario-based disease modeling and analytics toolkit used in the Philippines. We trace how evolving epidemiological analysis at the national, regional, and provincial levels guided government actions; and conversely, how emergent policy questions prompted subsequent model development and analysis. At various stages of the pandemic, simulated outputs of the FASSSTER model strongly correlated with empirically observed case trajectories ( = 94%–99%, \u3c .001). Model simulations were subsequently utilized to predict the outcomes of proposed interventions, including the calibration of community quarantine levels alongside improvements to healthcare system capacity. This study shows how the FASSSTER model enabled the implementation of a phased approach toward gradually expanding economic activity while limiting the spread of COVID-19. This work points to the importance of locally contextualized, flexible, and responsive mathematical modeling, as applied to pandemic intelligence and for data-driven policy-making in genera