Dengue in the Philippines: model and analysis of parameters affecting transmission

Application of a reaction-based water quality model to the total dissolved solids concentration of the Pasig River

With the goal to support effective water resource management, water quality models have gained popularity as tools for evaluating the distributions of pollutants and sediments. This work focuses on the application of the numerical solution of an advection-dispersion-reaction (ADR) water quality model for rivers and streams to a major Philippine waterway, the Pasig River. The water quality constituent is described by a system of reaction and advection-dispersion-reaction equations. The model and method are based on a previously used strategy where Guass-Jordan decomposition is applied to the matrix system and the resulting conservative form of the model is solved numerically using the fully implicit scheme and finite element method. The methodology is demonstrated by a case study in Pasig River involving the concentrations of total dissolved solids (TDS) obtained from the Department of Environment and Natural Resources (DENR) through the Pasig River Unified Monitoring Stations (PRUMS) report. Sensitivity analysis and parameter estimation are also applied to the model to assess which parameters influence the model output the most.

Impact of High Covid-19 Vaccination Rate in an Aging Population: Estimating Averted Hospitalizations and Deaths in the Basque Country, Spain Using Counterfactual Modeling

COVID-19 vaccines have demonstrated significant efficacy in reducing severe symptoms and fatalities, although their effectiveness in preventing transmission varies depending on the population's age profile and the dominant variant. This study evaluates the impact of the COVID-19 vaccination campaign in the Basque Country region of Spain, which has the fourth highest proportion of elderly individuals worldwide. Using epidemiological data on hospitalizations, ICU admissions, fatalities, and vaccination coverage, we calibrated four versions of an ordinary differential equations model with varying assumptions on the age structure and transmission function. Counterfactual no-vaccine scenarios were simulated by setting the vaccination rate to zero while all other parameters were held constant. The initial vaccination rollout is estimated to have prevented 46,000 to 75,000 hospitalizations, 6,000 to 11,000 ICU admissions, and 15,000 to 24,000 deaths, reducing these outcomes by 73-86%. The most significant impact occurred during the third quarter of 2021, coinciding with the Delta variant's dominance and a vaccination rate exceeding 60%. Sensitivity analysis revealed that vaccination coverage had a more substantial effect on averted outcomes than vaccine efficacy. Overall, the vaccination campaign in the Basque Country significantly reduced severe COVID-19 outcomes, aligning with global estimates and demonstrating robustness across different modeling approaches.

A game-theoretic model of lymphatic filariasis prevention

Lymphatic filariasis (LF) is a mosquito-borne parasitic neglected tropical disease. In 2000, WHO launched the Global Programme to Eliminate Lymphatic Filariasis (GPELF) as a public health problem. In 2020, new goals for 2030 were set which includes a reduction to 0 of the total population requiring Mass Drug Administrations (MDA), a primary tool of GPELF. We develop a mathematical model to study what can happen at the end of MDA. We use a game-theoretic approach to assess the voluntary use of insect repellents in the prevention of the spread of LF through vector bites. Our results show that when individuals use what they perceive as optimal levels of protection, the LF incidence rates will become high. This is in striking difference to other vector-borne NTDs such as Chagas or zika. We conclude that the voluntary use of the protection alone will not be enough to keep LF eliminated as a public health problem and a more coordinated effort will be needed at the end of MDA.

Constructing and validating a transferable epidemic risk index in data scarce environments using open data: A case study for dengue in the Philippines

Epidemics are among the most costly and destructive natural hazards globally. To reduce the impacts of infectious disease outbreaks, the development of a risk index for infectious diseases can be effective, by shifting infectious disease control from emergency response to early detection and prevention.

In this study, we introduce a methodology to construct and validate an epidemic risk index using only open data, with a specific focus on scalability. The external validation of our risk index makes use of distance sampling to correct for underreporting of infections, which is often a major source of biases, based on geographical accessibility to health facilities. We apply this methodology to assess the risk of dengue in the Philippines.

The results show that the computed dengue risk correlates well with standard epidemiological metrics, i.e. dengue incidence (p = 0.002). Here, dengue risk constitutes of the two dimensions susceptibility and exposure. Susceptibility was particularly associated with dengue incidence (p = 0.048) and dengue case fatality rate (CFR) (p = 0.029). Exposure had lower correlations to dengue incidence (p = 0.193) and CFR (p = 0.162). Highest risk indices were seen in the south of the country, mainly among regions with relatively high susceptibility to dengue outbreaks.

Our findings reflect that the modelled epidemic risk index is a strong indication of sub-national dengue disease patterns and has therefore proven suitability for disease risk assessments in the absence of timely epidemiological data. The presented methodology enables the construction of a practical, evidence-based tool to support public health and humanitarian decision-making processes with simple, understandable metrics. The index overcomes the main limitations of existing indices in terms of construction and actionability.

Epidemics are among the most costly and destructive natural hazards occurring globally; currently, the response to epidemics is still focused on reaction rather than prevention or preparedness. The development of an epidemic risk index can support identifying high-risk areas and can guide prioritization of preventive action and humanitarian response. While several frameworks for epidemic risk assessment exist, they suffer from several limitations, which resulted in limited uptake by local health actors—such as governments and humanitarian relief workers—in their decision-making processes. In this study, we present a methodology to develop epidemic risk indices, which overcomes the major limitations of previous work: strict data requirements, insufficient geographical granularity, validation against epidemiological data. We take as a case study dengue in the Philippines and develop an epidemic risk index; we correct dengue incidence for underreporting based on accessibility to healthcare and show that it correlates well with the risk index (Pearson correlation coefficient 0.69, p-value 0.002). Our methodology enables the development of disease-specific epidemic risk indices at a sub-national level, even in countries with limited data availability; these indices can guide local actors in programming prevention and response activities. Our findings on the case study show that the epidemic risk index is a strong indicator of sub-national dengue disease patterns and is therefore suitable for disease risk assessments in the absence of timely and complete epidemiological data.

A nonautonomous mathematical model to assess the impact of algae on the abatement of atmospheric carbon dioxide

The world’s oceans have played an important role in sequestering atmospheric carbon dioxide through solubility and the action of algae. Fixation of atmospheric carbon dioxide by photoautotrophic algal cultures has the potential to diminish the release of carbon dioxide into the atmosphere, thereby helping to alleviate the trend toward global warming. This work investigates the role of algae in controlling the level of atmospheric carbon dioxide. Partial Rank Correlation Coefficients (PRCCs) technique is used to address how the concentration of atmospheric carbon dioxide is affected by changes in a specific parameter disregarding the uncertainty over the rest of the model parameters. Parameters related to algal growth are shown to significantly reduce the level of atmospheric CO2. Further, we explore the dynamics of nonautonomous system by incorporating the seasonal variations of some ecologically important model parameters. Our nonautonomous system exhibits globally attractive positive periodic solution, and also the appearance of double periodic solution is observed. Moreover, by letting the seasonally forced parameters as almost periodic functions of time, we show almost periodic behavior of the system. Our findings suggest that the policy makers should focus on continuous addition of nutrients in the ocean to accelerate the algal growth thereby reducing the level of carbon dioxide in the atmosphere.

A mathematical model of the dynamics of lymphatic filariasis in Caraga Region, the Philippines

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.

Reviewing estimates of the basic reproduction number for dengue, Zika and chikungunya across global climate zones

BACKGROUND

Globally, dengue, Zika virus, and chikungunya are important viral mosquito-borne diseases that infect millions of people annually. Their geographic range includes not only tropical areas but also sub-tropical and temperate zones such as Japan and Italy. The relative severity of these arboviral disease outbreaks can vary depending on the setting. In this study we explore variation in the epidemiologic potential of outbreaks amongst these climatic zones and arboviruses in order to elucidate potential reasons behind such differences.

METHODOLOGY

We reviewed the peer-reviewed literature (PubMed) to obtain basic reproduction number (R₀) estimates for dengue, Zika virus, and chikungunya from tropical, sub-tropical and temperate regions. We also computed R₀ estimates for temperate and sub-tropical climate zones, based on the outbreak curves in the initial outbreak phase. Lastly we compared these estimates across climate zones, defined by latitude.

RESULTS

Of 2115 studies, we reviewed the full text of 128 studies and included 65 studies in our analysis. Our results suggest that the R₀ of an arboviral outbreak depends on climate zone, with lower R₀ estimates, on average, in temperate zones (R₀ = 2.03) compared to tropical (R₀ = 3.44) and sub-tropical zones (R₀ = 10.29). The variation in R₀ was considerable, ranging from 0.16 to 65. The largest R₀ was for dengue (65) and was estimated by the Ross-Macdonald model in the tropical zone, whereas the smallest R₀ (0.16) was for Zika virus and was estimated statistically from an outbreak curve in the sub-tropical zone.

CONCLUSIONS

The results indicate climate zone to be an important determinant of the basic reproduction number, R₀, for dengue, Zika virus, and chikungunya. The role of other factors as determinants of R₀, such as methods, environmental and social conditions, and disease control, should be further investigated. The results suggest that R₀ may increase in temperate regions in response to global warming, and highlight the increasing need for strengthening preparedness and control activities.