Reconstruction of 60 Years of Chikungunya Epidemiology in the Philippines Demonstrates Episodic and Focal Transmission.

Proper understanding of the long-term epidemiology of chikungunya has been hampered by poor surveillance. Outbreak years are unpredictable and cases often misdiagnosed. Here we analyzed age-specific data from 2 serological studies (from 1973 and 2012) in Cebu, Philippines, to reconstruct both the annual probability of infection and population-level immunity over a 60-year period (1952-2012). We also explored whether seroconversions during 2012-2013 were spatially clustered. Our models identified 4 discrete outbreaks separated by an average delay of 17 years. On average, 23% (95% confidence interval [CI], 16%-37%) of the susceptible population was infected per outbreak, with >50% of the entire population remaining susceptible at any point. Participants who seroconverted during 2012-2013 were clustered at distances of 350 000 infections were missed by surveillance systems. Serological studies could supplement surveillance to provide important insights on pathogen circulation

High rate of subclinical chikungunya virus infection and association of neutralizing antibody with protection in a prospective cohort in the Philippines.

BACKGROUND: Chikungunya virus (CHIKV) is a globally re-emerging arbovirus for which previous studies have indicated the majority of infections result in symptomatic febrile illness. We sought to characterize the proportion of subclinical and symptomatic CHIKV infections in a prospective cohort study in a country with known CHIKV circulation. METHODS/FINDINGS: A prospective longitudinal cohort of subjects ≥6 months old underwent community-based active surveillance for acute febrile illness in Cebu City, Philippines from 2012-13. Subjects with fever history were clinically evaluated at acute, 2, 5, and 8 day visits, and at a 3-week convalescent visit. Blood was collected at the acute and 3-week convalescent visits. Symptomatic CHIKV infections were identified by positive CHIKV PCR in acute blood samples and/or CHIKV IgM/IgG ELISA seroconversion in paired acute/convalescent samples. Enrollment and 12-month blood samples underwent plaque reduction neutralization test (PRNT) using CHIKV attenuated strain 181/clone25. Subclinical CHIKV infections were identified by ≥8-fold rise from a baseline enrollment PRNT titer 50 years old. Baseline CHIKV PRNT titer ≥10 was associated with 100% (95%CI: 46.1, 100.0) protection from symptomatic CHIKV infection. Phylogenetic analysis demonstrated Asian genotype closely related to strains from Asia and the Caribbean. CONCLUSIONS: Subclinical infections accounted for a majority of total CHIKV infections. A positive baseline CHIKV PRNT titer was associated with protection from symptomatic CHIKV infection. These findings have implications for assessing disease burden, understanding virus transmission, and supporting vaccine development

26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 - Meeting Abstracts - Antwerp, Belgium. 15–20 July 2017

This work was produced as part of the activities of FAPESP Research,\ud Disseminations and Innovation Center for Neuromathematics (grant\ud 2013/07699-0, S. Paulo Research Foundation). NLK is supported by a\ud FAPESP postdoctoral fellowship (grant 2016/03855-5). ACR is partially\ud supported by a CNPq fellowship (grant 306251/2014-0)

Epidemiology of dengue disease in the Philippines (2000-2011): a systematic literature review.

This literature analysis describes the available dengue epidemiology data in the Philippines between 2000 and 2011. Of 253 relevant data sources identified, 34, including additional epidemiology data provided by the National Epidemiology Center, Department of Health, Philippines, were reviewed. There were 14 publications in peer reviewed journals, and 17 surveillance reports/sources, which provided variable information from the passive reporting system and show broad trends in dengue incidence, including age group predominance and disease severity. The peer reviewed studies focused on clinical severity of cases, some revealed data on circulating serotypes and genotypes and on the seroepidemiology of dengue including incidence rates for infection and apparent disease. Gaps in the data were identified, and include the absence incidence rates stratified by age, dengue serotype and genotype distribution, disease severity data, sex distribution data, and seroprevalence data

The epidemiology of dengue disease in the Philippines, 2000–2011.

<p>A: The number of reported dengue disease cases and incidence per 100,000 population. B: The number of reported deaths attributed to dengue disease and CFR per 100 cases. The reported number of dengue disease cases in the Philippines fluctuated throughout the review period, with an overall increase in incidence observed over time. Peaks in dengue disease cases occurred in 2001, 2003 and 2007. Dengue disease-related deaths fluctuated, peaking in 2006. Overall, the CFR was in the range 0.5–1.2 per 100 cases and decreased after 2005. Using available data* from the DoH <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0003027#pntd.0003027-Department1" target="_blank">[5]</a>, <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0003027#pntd.0003027-Department3" target="_blank">[25]</a>–<a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0003027#pntd.0003027-Department7" target="_blank">[29]</a> and the WHO <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0003027#pntd.0003027-World1" target="_blank">[6]</a>, <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0003027#pntd.0003027-LeeSuy1" target="_blank">[16]</a>, <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0003027#pntd.0003027-World4" target="_blank">[20]</a>, <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0003027#pntd.0003027-World5" target="_blank">[21]</a>, <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0003027#pntd.0003027-World6" target="_blank">[30]</a>–<a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0003027#pntd.0003027-World8" target="_blank">[32]</a>. Sources: Number of reported cases: 2000–2005: DoH 2000–2005 <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0003027#pntd.0003027-Department3" target="_blank">[25]</a>; 2006–2007: FHSIS 2000–2009 <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0003027#pntd.0003027-Department7" target="_blank">[29]</a>; 2008–2011: WHO data: WHO 2008 <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0003027#pntd.0003027-LeeSuy1" target="_blank">[16]</a>, 2009 <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0003027#pntd.0003027-World4" target="_blank">[20]</a>, 2012 <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0003027#pntd.0003027-World7" target="_blank">[31]</a>, <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0003027#pntd.0003027-World8" target="_blank">[32]</a> (2010 and 2011 values estimated from graph); Incidence: 2000–2007: FHSIS 2000–2009 <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0003027#pntd.0003027-Department7" target="_blank">[29]</a>; Deaths: 2000–2005: DoH 2000–2005 <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0003027#pntd.0003027-Department3" target="_blank">[25]</a>; 2006: DoH 2011 <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0003027#pntd.0003027-Department6" target="_blank">[28]</a>; 2007–2010: Arima and Matsuia 2011 <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0003027#pntd.0003027-Arima1" target="_blank">[33]</a>; 2011: WHO 2012 <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0003027#pntd.0003027-World7" target="_blank">[31]</a> (value estimated from graph); CFR: 2000–2005: WHO 2008 <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0003027#pntd.0003027-LeeSuy1" target="_blank">[16]</a>; 2006–2010: Arima and Matsuia 2011 <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0003027#pntd.0003027-Arima1" target="_blank">[33]</a>; 2011: WHO 2012 <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0003027#pntd.0003027-World7" target="_blank">[31]</a> (value estimated from graph). *Data on all dengue disease cases were not publically available from the DoH between 2008 and 2011. CFR, case fatality rate; DoH, Department of Health; WHO, World Health Organization.</p

Dengue virus serotype distribution in the Philippines: regional studies.

<p>All four DENV serotypes were reportedly present in the Philippines at some time during the review period <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0003027#pntd.0003027-YpilButac1" target="_blank">[43]</a>–<a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0003027#pntd.0003027-Salda1" target="_blank">[50]</a> but the predominant serotypes changed from DENV-1 and -2 early in the review period <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0003027#pntd.0003027-Carlos1" target="_blank">[47]</a>, <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0003027#pntd.0003027-Oishi1" target="_blank">[48]</a>, <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0003027#pntd.0003027-Salda1" target="_blank">[50]</a> to DENV-3 towards the end of the review period <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0003027#pntd.0003027-YpilButac1" target="_blank">[43]</a>–<a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0003027#pntd.0003027-Libraty1" target="_blank">[46]</a>, <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0003027#pntd.0003027-Alera1" target="_blank">[49]</a>.</p

Map of the Philippines showing the administrative 17 regions [19], [20].

<p>The Republic of the Philippines is an archipelago in Southeast Asia consisting of 7107 islands. The country is divided into 17 regions within the three island groups of Luzon (Regions I–V, Cordillera Administrative Region [CAR] and National Capital Region [NCR]), Visayas (Regions VI–VIII) and Mindanao (Regions IX–XIII and Autonomous Region in Muslim Mindanao). Metro Manila is the metropolitan area that contains the City of Manila, the capital of the Philippines. The metropolis is officially called the National Capital Region (NCR, the term used throughout this report) and is composed of Manila plus 16 neighboring cities and municipalities, including Quezon City.</p

Results of literature search and evaluation of identified studies according to PRISMA.

<p>All references identified in the on-line database searches were assigned a unique identification number. Following the removal of duplicates and articles that did not satisfy the inclusion criteria from review of the titles and abstracts, the full papers of the first selection of references were retrieved either electronically or in paper form. A further selection was made based on review of the full text of the articles. DoH, Department of Health; EMBASE, Excerpta Medica Database; IMSEAR, Index Medicus for South East-Asia Region; Medline, United States National Library of Medicine and the National Institutes of Health Medical Database; PRISMA, preferred reporting items of systematic reviews and meta-analyses; WHOLIS, World Health Organization Library database; WHOSEAR, World Health Organization Regional Office for Southeast Asia; WPRO, World Health Organization Western Pacific Region.</p

Hepatitis B seroprevalence among 5 to 6 years old children in the Philippines born prior to routine hepatitis B vaccination at birth

To assess the prevalence of hepatitis B in the Philippines, we conducted a cross-sectional study among 5 to 6 year old children born in 2007–2008, when the birth dose started to be implemented in the country. The study was conducted from 25 July to 22 October 2013 in 24 provinces and used a 3-stage cluster design and probability-proportional to size sampling. Blood was obtained and sera were tested for hepatitis B surface antigen (HBsAg). The survey included 2,769 children, of whom 26% received a timely birth dose (within 24 hours of birth) and 89% received 3 doses of the hepatitis B vaccine. Due to problems in the initial testing algorithm, only 2,407 sera were available for HBsAg testing, 20 (weighted%, 0.86%) were HBsAg positive. By immunization card and recall, among HBsAg positive children, 2 (weighted%, 20%) received a timely birth dose while 17 (weighted%, 85%) received 3 doses of the hepatitis B vaccine. The seroprevalence of HBsAg that we detected was lower than expected. However, there were several limitations in the field and in the laboratory that may have affected the representativeness of the results. Follow up studies need to be conducted to validate these results

Prediction of high incidence of dengue in the Philippines.

BACKGROUND: Accurate prediction of dengue incidence levels weeks in advance of an outbreak may reduce the morbidity and mortality associated with this neglected disease. Therefore, models were developed to predict high and low dengue incidence in order to provide timely forewarnings in the Philippines. METHODS: Model inputs were chosen based on studies indicating variables that may impact dengue incidence. The method first uses Fuzzy Association Rule Mining techniques to extract association rules from these historical epidemiological, environmental, and socio-economic data, as well as climate data indicating future weather patterns. Selection criteria were used to choose a subset of these rules for a classifier, thereby generating a Prediction Model. The models predicted high or low incidence of dengue in a Philippines province four weeks in advance. The threshold between high and low was determined relative to historical incidence data. PRINCIPAL FINDINGS: Model accuracy is described by Positive Predictive Value (PPV), Negative Predictive Value (NPV), Sensitivity, and Specificity computed on test data not previously used to develop the model. Selecting a model using the F0.5 measure, which gives PPV more importance than Sensitivity, gave these results: PPV = 0.780, NPV = 0.938, Sensitivity = 0.547, Specificity = 0.978. Using the F3 measure, which gives Sensitivity more importance than PPV, the selected model had PPV = 0.778, NPV = 0.948, Sensitivity = 0.627, Specificity = 0.974. The decision as to which model has greater utility depends on how the predictions will be used in a particular situation. CONCLUSIONS: This method builds prediction models for future dengue incidence in the Philippines and is capable of being modified for use in different situations; for diseases other than dengue; and for regions beyond the Philippines. The Philippines dengue prediction models predicted high or low incidence of dengue four weeks in advance of an outbreak with high accuracy, as measured by PPV, NPV, Sensitivity, and Specificity