Understanding COVID-19 dynamics and the effects of interventions in the Philippines: A mathematical modelling study

Health Protocol Practices and Personal Preventive Measures among Fully Vaccinated Individuals with Comorbidities in the National Capital Region, Philippines during the COVID-19 Pandemic: A Mixed-Method Study

Background and Objective

The Philippine Inter-Agency Task Force for the Management of Emerging Infectious Diseases implemented health protocol guidelines to reduce the risk of COVID-19 transmission. Individuals with comorbidities were advised to take precautionary measures due to their increased vulnerability. This study aimed to assess the relationship between knowledge, acceptance, and adherence to health protocols among fully vaccinated individuals with comorbidities in the National Capital Region, Philippines.

Methods

The study employed an explanatory-sequential mixed-method design. The quantitative phase involved an online survey with 384 respondents. The survey included questions on socio-demographic profile, COVID-19 knowledge, acceptability of health protocols, and adherence to preventive practices. Chi-square Test of Independence and Pearson's Correlation Test were used to analyze the data. Semi-structured interviews were conducted with 11 participants, providing rich insights into their personal experiences. The interview transcripts were analyzed using Colaizzi's descriptive method with the aid of qualitative analysis software (MAXQDA), ensuring a rigorous approach to thematic analysis. The integration of the two phases was achieved by connecting quantitative results with qualitative insights, creating a comprehensive understanding of the phenomena under study.

Results

Findings showed that the relationship of socio-demographic characteristics and level of knowledge (Gender p<0.05; Employment status p<0.05), and level of acceptability to minimum health protocols and personal preventive practices varies depending on the respective health protocol practice. The level of knowledge about COVID-19 was positively correlated with knowledge of minimum health protocols (p<0.01). Similarly, knowledge and acceptability were dependent on adherence to most health protocols. The qualitative analysis identified seven themes: Unmasking a collective mystery, Knowledge is part of weaponry, Safeguards for security, Tethered by a boundary, Individual cloaks of safety, The thread in the tapestry, and Towards the end of one story that described the participants' experiences, leading to the formulation of a Swiss Cheese Model of Health Protocol Practices.

Conclusion

The study suggests that multiple factors contribute to non-adherence to health protocols. Recognizing these holes and weaknesses in the COVID-19 pandemic response stresses the need for national leaders to place urgency on properly implementing preventive measures and providing health education to the masses during public health situations. Collaboration from all sectors is crucial in addressing public health crises. This study can be a valuable resource for future researchers, local government units, and policymakers in prioritizing public health care and pandemic preparedness.

A data science pipeline applied to Australia's 2022 COVID-19 Omicron waves

The field of software engineering is advancing at astonishing speed, with packages now available to support many stages of data science pipelines. These packages can support infectious disease modelling to be more robust, efficient and transparent, which has been particularly important during the COVID-19 pandemic. We developed a package for the construction of infectious disease models, integrated it with several open-source libraries and applied this composite pipeline to multiple data sources that provided insights into Australia's 2022 COVID-19 epidemic. We aimed to identify the key processes relevant to COVID-19 transmission dynamics and thereby develop a model that could quantify relevant epidemiological parameters. The pipeline's advantages include markedly increased speed, an expressive application programming interface, the transparency of open-source development, easy access to a broad range of calibration and optimisation tools and consideration of the full workflow from input manipulation through to algorithmic generation of the publication materials. Extending the base model to include mobility effects slightly improved model fit to data, with this approach selected as the model configuration for further epidemiological inference. Under our assumption of widespread immunity against severe outcomes from recent vaccination, incorporating an additional effect of the main vaccination programs rolled out during 2022 on transmission did not further improve model fit. Our simulations suggested that one in every two to six COVID-19 episodes were detected, subsequently emerging Omicron subvariants escaped 30-60% of recently acquired natural immunity and that natural immunity lasted only one to eight months on average. We documented our analyses algorithmically and present our methods in conjunction with interactive online code notebooks and plots. We demonstrate the feasibility of integrating a flexible domain-specific syntax library with state-of-the-art packages in high performance computing, calibration, optimisation and visualisation to create an end-to-end pipeline for infectious disease modelling. We used the resulting platform to demonstrate key epidemiological characteristics of the transition from the emergency to the endemic phase of the COVID-19 pandemic.

Analyzing the dynamics of COVID-19 transmission in select regions of the Philippines: A modeling approach to assess the impact of various tiers of community quarantines

The COVID-19 pandemic has significantly impacted communities worldwide, and effective management strategies are critical to reduce transmission rates and minimize the impact of the disease. In this study, we modeled and analyzed the COVID-19 transmission dynamics and derived relevant epidemiological values for three regions of the Philippines, namely, the National Capital Region (NCR), Davao City, and Baguio City, under different community quarantine implementations. The unique features and differences of these regions-of-interest were accounted for in simulating the disease spread and in estimating key epidemiological parameters fitted to the reported COVID-19 cases. Results support the robustness of the model formulated and provides insights into the effect of the government's implemented intervention protocols. With a forecasting feature, this modeling framework is beneficial for science-based decision support, policy making, and assessment for recent and future pandemics wherever regions-of-interest.

Modelling COVID-19 transmission dynamics in Laos under non-pharmaceutical interventions, vaccination, and replacement of SARS-CoV-2 variants

BACKGROUND

Understanding how the COVID-19 pandemic evolved under control measures is crucial to tackle the SARS-CoV-2 virus spread. Laos, a country bordering China but with late occurrence and low burden of COVID-19 compared to its neighbouring countries, was used for a case study.

METHODS

A transmission model with disease reporting was proposed to investigate the impact of control measures on the SARS-CoV-2 virus spread in Laos from April 2021 to May 2022. It was assumed that the transmission rate changed with people's behaviours, control measures and emerging variants; susceptibility decreased with vaccination and infection. Bayesian inference was used for model calibration to data of confirmed cases, deaths, and recoveries, and the deviance information criterion was used to select the best model variant.

RESULTS

Our model including Non-pharmaceutical interventions (NPIs), behaviour change, vaccination, and changing variants well explained the three waves in Laos. The Alpha variant was estimated to have a basic reproduction number of 1.55 (95% CrI: 1.47-1.64) and was replaced by the Delta variant from September 2021 which was 1.88 (95% CrI: 1.77-2.01) times more transmissible; the Delta variant was replaced by Omicron variant from March 2022 which was 3.33 (95% CrI: 2.84-3.74) times more transmissible. The Delta variant was the most severe with a case fatality rate of 1.05% (95% CrI: 0.96-1.15%) while the Alpha variant and Omicron variant were much milder. The ascertainment rate was low and variable: first decreasing from 13.2 to 1.8% by 23 May 2021, and then increasing to 23.4% by 15 March 2022. Counterfactual simulations indicated that vaccination played strong roles in reducing infections even under the emergence of immune escape variants while behaviour change delayed but might not flatten the peak of outbreaks.

CONCLUSIONS

The three waves of Laos' epidemics were due to the invasion of more transmissible and immune escape variants that affected the herd immunity built via vaccination and infection. Even with immunity waning and the escape of new variants, vaccination was still the major contributor to control COVID-19 and combining behaviour changes and vaccination would best suppress future outbreaks of COVID-19.

Classification modeling of intention to donate for victims of Typhoon Odette using deep learning neural network

The need for stability in the economy for world development has been a challenge due to the COVID-19 pandemic. In addition, the increase of natural disasters and their aftermath have been increasing causing damages to infrastructure, the economy, livelihood, and lives in general. This study aimed to determine factors affecting the intention to donate for victims of Typhoon Odette, a recent super typhoon that hit the Philippines leading to affect 38 out of 81 provinces of the most natural disaster-prone countries. Determining the most significant factor affecting the intention to donate may help in increasing the engagement of donations among other people to help establish a more stable economy to heighten world development. With the use of deep learning neural network, a 97.12% accuracy was obtained for the classification model. It could be deduced that when donors understand and perceive both severity and vulnerability to be massive and highly damaging, then a more positive intention to donate to victims of typhoons will be observed. In addition, the influence of other people, the holiday season when the typhoon happened, and the media as a platform have greatly contributed to heightening the intention to donate and control over the donor's behavior. The findings of this study could be applied and utilized by government agencies and donation platforms to help engage and promote communication among donors. Moreover, the framework and methodology considered in this study may be extended to evaluate intention, natural disasters, and behavioral studies worldwide.

Adherence to COVID-19 health protocols in an online news context in the Philippines: A manifest content analysis

OBJECTIVES

Globally, adherence to COVID-19 health and safety protocols played a crucial role in preventing the spread of the virus. Thus, this study analyzed online news articles reporting adherence to COVID-19 health and safety protocols in the Philippines.

DESIGN

Manifest content analysis.

SAMPLE

News articles (n = 192) from three major online news portals in the Philippines.

MEASUREMENT

Published online news articles were collected during the peak of the COVID-19 pandemic (March 2020 to March 2021). Bengtsson's content analysis approach was used to analyze the data. Member-checking and intercoder reliability validated the study's results.

RESULTS

Three main themes emerged: (a) adherence, (b) non-adherence, and (c) partial adherence. The subthemes were labeled who, what, when, where, and why. The same behavior, social distancing, was the most adhered to and non-adhered COVID-19 health protocol. This protocol has the highest occurrences in political protest, religious-related activities, and self-initiated quarantine. Leisure activities both showed non-adherence and partial adherence.

CONCLUSIONS

Online news articles depicted Filipinos' adherence to health and safety protocols. Their adherence was primarily determined by one's group or community, social norms, and values. The government and its public health agencies should strengthen current efforts and continuously re-evaluate existing policies to modify ineffective and confusing safety health protocols.

Utilization of Random Forest Classifier and Artificial Neural Network for Predicting Factors Influencing the Perceived Usability of COVID-19 Contact Tracing “MorChana” in Thailand

With the constant mutation of COVID-19 variants, the need to reduce the spread should be explored. MorChana is a mobile application utilized in Thailand to help mitigate the spread of the virus. This study aimed to explore factors affecting the actual use (AU) of the application through the use of machine learning algorithms (MLA) such as Random Forest Classifier (RFC) and Artificial Neural Network (ANN). An integrated Protection Motivation Theory (PMT) and the Unified Theory of Acceptance and Use of Technology (UTAUT) were considered. Using convenience sampling, a total of 907 valid responses from those who answered the online survey were voluntarily gathered. With 93.00% and 98.12% accuracy from RFC and ANN, it was seen that hedonic motivation and facilitating conditions were seen to be factors affecting very high AU; while habit and understanding led to high AU. It was seen that when people understand the impact and causes of the COVID-19 pandemic’s aftermath, its severity, and also see a way to reduce it, it would lead to the actual usage of a system. The findings of this study could be used by developers, the government, and stakeholders to capitalize on using the health-related applications with the intention of increasing actual usage. The framework and methodology used presented a way to evaluate health-related technologies. Moreover, the developing trends of using MLA for evaluating human behavior-related studies were further justified in this study. It is suggested that MLA could be utilized to assess factors affecting human behavior and technology used worldwide.

Projections of the transmission of the Omicron variant for Toronto, Ontario, and Canada using surveillance data following recent changes in testing policies

At the end of 2021, with the rapid escalation of COVID19 cases due to the Omicron variant, testing centers in Canada were overwhelmed. To alleviate the pressure on the PCR testing capacity, many provinces implemented new strategies that promote self testing and adjust the eligibility for PCR tests, making the count of new cases underreported. We designed a novel compartmental model which captures the new testing guidelines, social behaviours, booster vaccines campaign and features of the newest variant Omicron. To better describe the testing eligibility, we considered the population divided into high risk and non-high-risk settings. The model is calibrated using data from January 1 to February 9, 2022, on cases and severe outcomes in Canada, the province of Ontario and City of Toronto. We conduct analyses on the impact of PCR testing capacity, self testing, different levels of reopening and vaccination coverage on cases and severe outcomes. Our results show that the total number of cases in Canada, Ontario and Toronto are 2.34 (95%CI: 1.22-3.38), 2.20 (95%CI: 1.15-3.72), and 1.97(95%CI: 1.13-3.41), times larger than reported cases, respectively. The current testing strategy is efficient if partial restrictions, such as limited capacity in public spaces, are implemented. Allowing more people to have access to PCR reduces the daily cases and severe outcomes; however, if PCR test capacity is insufficient, then it is important to promote self testing. Also, we found that reopening to a pre-pandemic level will lead to a resurgence of the infections, peaking in late March or April 2022. Vaccination and adherence to isolation protocols are important supports to the testing policies to mitigate any possible spread of the virus.

Utilization of Random Forest and Deep Learning Neural Network for Predicting Factors Affecting Perceived Usability of a COVID-19 Contact Tracing Mobile Application in Thailand "ThaiChana"

The continuous rise of the COVID-19 Omicron cases despite the vaccination program available has been progressing worldwide. To mitigate the COVID-19 contraction, different contact tracing applications have been utilized such as Thai Chana from Thailand. This study aimed to predict factors affecting the perceived usability of Thai Chana by integrating the Protection Motivation Theory and Technology Acceptance Theory considering the System Usability Scale, utilizing deep learning neural network and random forest classifier. A total of 800 respondents were collected through convenience sampling to measure different factors such as understanding COVID-19, perceived severity, perceived vulnerability, perceived ease of use, perceived usefulness, attitude towards using, intention to use, actual system use, and perceived usability. In total, 97.32% of the deep learning neural network showed that understanding COVID-19 presented the most significant factor affecting perceived usability. In addition, random forest classifier produced a 92% accuracy with a 0.00 standard deviation indicating that understanding COVID-19 and perceived vulnerability led to a very high perceived usability while perceived severity and perceived ease of use also led to a high perceived usability. The findings of this study could be considered by the government to promote the usage of contact tracing applications even in other countries. Finally, deep learning neural network and random forest classifier as machine learning algorithms may be utilized for predicting factors affecting human behavior in technology or system acceptance worldwide.

Sustaining effective COVID-19 control in Malaysia through large-scale vaccination

INTRODUCTION

As of 3rd June 2021, Malaysia is experiencing a resurgence of COVID-19 cases. In response, the federal government has implemented various non-pharmaceutical interventions (NPIs) under a series of Movement Control Orders and, more recently, a vaccination campaign to regain epidemic control. In this study, we assessed the potential for the vaccination campaign to control the epidemic in Malaysia and four high-burden regions of interest, under various public health response scenarios.

METHODS

A modified susceptible-exposed-infectious-recovered compartmental model was developed that included two sequential incubation and infectious periods, with stratification by clinical state. The model was further stratified by age and incorporated population mobility to capture NPIs and micro-distancing (behaviour changes not captured through population mobility). Emerging variants of concern (VoC) were included as an additional strain competing with the existing wild-type strain. Several scenarios that included different vaccination strategies (i.e. vaccines that reduce disease severity and/or prevent infection, vaccination coverage) and mobility restrictions were implemented.

RESULTS

The national model and the regional models all fit well to notification data but underestimated ICU occupancy and deaths in recent weeks, which may be attributable to increased severity of VoC or saturation of case detection. However, the true case detection proportion showed wide credible intervals, highlighting incomplete understanding of the true epidemic size. The scenario projections suggested that under current vaccination rates complete relaxation of all NPIs would trigger a major epidemic. The results emphasise the importance of micro-distancing, maintaining mobility restrictions during vaccination roll-out and accelerating the pace of vaccination for future control. Malaysia is particularly susceptible to a major COVID-19 resurgence resulting from its limited population immunity due to the country's historical success in maintaining control throughout much of 2020.

Impact of vaccine supplies and delays on optimal control of the COVID-19 pandemic: mapping interventions for the Philippines

BACKGROUND

Around the world, controlling the COVID-19 pandemic requires national coordination of multiple intervention strategies. As vaccinations are globally introduced into the repertoire of available interventions, it is important to consider how changes in the local supply of vaccines, including delays in administration, may be addressed through existing policy levers. This study aims to identify the optimal level of interventions for COVID-19 from 2021 to 2022 in the Philippines, which as a developing country is particularly vulnerable to shifting assumptions around vaccine availability. Furthermore, we explore optimal strategies in scenarios featuring delays in vaccine administration, expansions of vaccine supply, and limited combinations of interventions.

METHODS

Embedding our work within the local policy landscape, we apply optimal control theory to the compartmental model of COVID-19 used by the Philippine government's pandemic surveillance platform and introduce four controls: (a) precautionary measures like community quarantines, (b) detection of asymptomatic cases, (c) detection of symptomatic cases, and (d) vaccinations. The model is fitted to local data using an L-BFGS minimization procedure. Optimality conditions are identified using Pontryagin's minimum principle and numerically solved using the forward-backward sweep method.

RESULTS

Simulation results indicate that early and effective implementation of both precautionary measures and symptomatic case detection is vital for averting the most infections at an efficient cost, resulting in [Formula: see text] reduction of infections compared to the no-control scenario. Expanding vaccine administration capacity to 440,000 full immunizations daily will reduce the overall cost of optimal strategy by [Formula: see text], while allowing for a faster relaxation of more resource-intensive interventions. Furthermore, delays in vaccine administration require compensatory increases in the remaining policy levers to maintain a minimal number of infections. For example, delaying the vaccines by 180 days (6 months) will result in an [Formula: see text] increase in the cost of the optimal strategy.

CONCLUSION

We conclude with practical insights regarding policy priorities particularly attuned to the Philippine context, but also applicable more broadly in similar resource-constrained settings. We emphasize three key takeaways of (a) sustaining efficient case detection, isolation, and treatment strategies; (b) expanding not only vaccine supply but also the capacity to administer them, and; (c) timeliness and consistency in adopting policy measures.