Effective Epidemic Control Research Articles

Strategies to Overcome Erroneous Outcomes in Reverse Transcription-Polymerase Chain Reaction (RT-PCR) Testing: Insights From the COVID-19 Pandemic.

The reverse transcription-polymerase chain reaction (RT-PCR) test to detect SARS-CoV-2, the virus causing COVID-19, has been regarded as the diagnostic gold standard. However, the excessive sensitivity of RT-PCR may cause false-positive outcomes from contamination. Again, its technical complexity increases the chances of false-negatives due to pre-analytical and analytical errors. This narrative review explores the elements contributing to inaccurate results during the COVID-19 pandemic and offers strategies to minimize these errors. False-positive results may occur due to specimen contamination, non-specific primer binding, residual viral RNA, and false-negatives, which may arise from improper sampling, timing, labeling, storage, low viral loads, mutations, and faulty test kits. Proposed mitigation strategies to enhance the accuracy of RT-PCR testing include comprehensive staff training in specimen collection, optimizing the timing of tests, analyzing multiple gene targets, incorporating clinical findings, workflow automation, and implementing stringent contamination control measures. Identifying and rectifying sources of error in RT-PCR diagnosis through quality control and standardized protocols is imperative for ensuring quality patient care and effective epidemic control.

Exploring the influence of environmental indicators and forecasting influenza incidence using ARIMAX models.

Influenza is a respiratory infection that poses a significant health burden worldwide. Environmental indicators, such as air pollutants and meteorological factors, play a role in the onset and propagation of influenza. Accurate predictions of influenza incidence and understanding the factors influencing it are crucial for public health interventions. Our study aims to investigate the impact of various environmental indicators on influenza incidence and apply the ARIMAX model to integrate these exogenous variables to enhance the accuracy of influenza incidence predictions. Descriptive statistics and time series analysis were employed to illustrate changes in influenza incidence, air pollutants, and meteorological indicators. Cross correlation function (CCF) was used to evaluate the correlation between environmental indicators and the influenza incidence. We used ARIMA and ARIMAX models to perform predictive analysis of influenza incidence. From January 2014 to September 2023, a total of 21,573 cases of influenza were reported in Fuzhou, with a noticeable year-by-year increase in incidence. The peak of influenza typically occurred around January each year. The results of CCF analysis showed that all 10 environmental indicators had a significant impact on the incidence of influenza. The ARIMAX(0, 0, 1) (1, 0, 0)12 with PM10(lag5) model exhibited the best prediction performance, as indicated by the lowest AIC, AICc, and BIC values, which were 529.740, 530.360, and 542.910, respectively. The model achieved a fitting RMSE of 2.999 and a predicting RMSE of 12.033. This study provides insights into the impact of environmental indicators on influenza incidence in Fuzhou. The ARIMAX(0, 0, 1) (1, 0, 0)12 with PM10(lag5) model could provide a scientific basis for formulating influenza control policies and public health interventions. Timely prediction of influenza incidence is essential for effective epidemic control strategies and minimizing disease transmission risks.

Deciphering the enigma of Lassa virus transmission dynamics and strategies for effective epidemic control through awareness campaigns and rodenticides

This study aims to formulate a mathematical framework to examine how the Lassa virus spreads in humans of opposite genders. The stability of the model is analyzed at an equilibrium point in the absence of the Lassa fever. The model’s effectiveness is evaluated using real-life data, and all the parameters needed to determine the basic reproduction number are estimated. Sensitivity analysis is performed to pinpoint the crucial parameters significantly influencing the spread of the infection. The interaction between threshold parameters and the basic reproduction number is simulated. Control theory is employed to devise and evaluate strategies, such as awareness campaigns, advocating condom usage, and deploying rodenticides to reduce the possibility of virus transmission efficiently.

Measles Outbreak in Socioeconomically Diverse Sections: A Review.

Measles outbreaks among socioeconomically weaker sections constitute a significant public health challenge.The objective is to highlight the specific vulnerabilities and contributing factors that make these communities more susceptible to measles outbreaks. Socioeconomically weaker sections, often characterized by poverty, inadequate healthcare access, overcrowding, and suboptimal immunization rates, face heightened risks of measles outbreaks. These outbreaks occur due to various interrelated factors, including limited healthcare infrastructure, low vaccine coverage, a lack of awareness about vaccination benefits, and difficulties in accessing healthcare services. The effects of measles outbreaks in socioeconomically disadvantaged areas are critical. Particularly among vulnerable groups, including newborns, expectant mothers, and malnourished people, measles increases morbidity and mortality. There is also a considerable financial impact on the healthcare system and the afflicted families. Measles outbreaks in these populations must be addressed using a diversified strategy. In order to improve vaccine coverage through targeted immunization programs, raise community vaccination awareness, and address social determinants of health, efforts should concentrate on bolstering the healthcare infrastructure. Effective epidemic control and prevention depend on coordinated efforts by healthcare practitioners, legislators, local leaders, and public health groups. Healthcare systems can lessen the impact of measles in socioeconomically disadvantaged areas and strive toward attaining equitable health outcomes for all populations by addressing the socioeconomic variables that lead to the vulnerability of measles.

Adaptive sequential surveillance with network and temporal dependence.

Strategic test allocation is important for control of both emerging and existing pandemics (eg, COVID-19, HIV). It supports effective epidemic control by (1) reducing transmission via identifying cases and (2) tracking outbreak dynamics to inform targeted interventions. However, infectious disease surveillance presents unique statistical challenges. For instance, the true outcome of interest (positive infection status) is often a latent variable. In addition, presence of both network and temporal dependence reduces data to a single observation. In this work, we study an adaptive sequential design, which allows for unspecified dependence among individuals and across time. Our causal parameter is the mean latent outcome we would have obtained, if, starting at time t given the observed past, we had carried out a stochastic intervention that maximizes the outcome under a resource constraint. The key strength of the method is that we do not have to model network and time dependence: a short-term performance Online Super Learner is used to select among dependence models and randomization schemes. The proposed strategy learns the optimal choice of testing over time while adapting to the current state of the outbreak and learning across samples, through time, or both. We demonstrate the superior performance of the proposed strategy in an agent-based simulation modeling a residential university environment during the COVID-19 pandemic.

CHINA'S CHOICE OF SECTORS FOR INVESTMENT IN THE “BELT AND ROAD” IN THE POST-EPIDEMIC ERA

In the post-epidemic era, Chinese investment in the “Belt and Road” countries faces significant opportunities and challenges. Firstly, global markets actively seeking recovery have given multinational investors more opportunities. China, with its strict and effective epidemic control, has preceded the rest of the world in the period of epidemic stabilization and Chinese investors have also seized the opportunity of economic reversal and recovery. However, it is worth noting that protectionism and anti-globalization forces around the world have also taken advantage of the COVID-19 epidemic to become more prevalent, and Chinese outbound investment will face a more complex environment and more intense confrontation. In this complex environment, choosing to develop investment sectors that are more compatible with the interests of the host country can effectively reduce the risk of investment. Therefore, this paper analyses the selection of industries for China's investment along the “Belt and Road” from multi-dimensional perspectives. SWOT analysis is used to explore the advantages and disadvantages of industries in the process of "going global" in four dimensions: opportunities, advantages, disadvantages and challenges. Finally, recommendations are given for the selection of industries for China's investments in countries along the “Belt and Road”. Key words: Belt and Road, China's investments, post-pandemic

Risk Perceptions, Level of Government Trust, and Psychological Distress During COVID-19 Pandemic in Taiwan.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is spreading worldwide, causing mental health concerns among people. People's perceptions of the disease affect their psychological adaptation and health outcomes. In this study, we present people's perceptions of coronavirus disease 2019 (COVID-19), level of government trust, and their psychological distress during the pandemic for examining the impact of peoples' COVID-19 perceptions on their mental health. This cross-sectional study was conducted through a telephone survey in Taiwan in April 2020. Participants were randomly selected for telephone screening using a computer-assisted telephone interviewer system. A total of 1098 participants aged more than 20 years participated in the survey. The mean age of participants was 47.7 ± 16.4 years. After controlling for covariates, participants who were worried about contracting COVID-19, those who believed that they had a chance of being infected with COVID-19, those who were reluctant to visit the hospital for fear of contracting the virus, those who felt that the pandemic had affected their daily life, and those with low levels of trust in the government's capacity to manage the pandemic had anxiety, hostility, depression, interpersonal sensitivity/inferiority, and psychological symptoms. People's perception of COVID-19 and public's trust in the government's ability to respond to the pandemic are related to psychological distress. Although the Taiwanese government may have undertaken effective epidemic control measures to address with the COVID-19 pandemic, this crisis may have still caused mental health problems in the general population. Health professionals and policy makers should pay more attention to high-risk groups among those at risk for developing mental health problems.

Manually-operated, slider cassette for multiplexed molecular detection at the point of care

Effective control of epidemics, individualized medicine, and new drugs with virologic response-dependent dose and timing require, among other things, simple, inexpensive, multiplexed molecular detection platforms suitable for point of care and home use. Herein, we describe our progress towards developing such a platform that includes sample lysis, nucleic acid isolation, concentration, purification, and amplification. Our diagnostic device comprises a sliding component that houses the nucleic acid isolation membrane and a housing containing three amplification reaction chambers with dry stored reagents, blisters with buffers and wash solutions, and absorption pads to facilitate capillarity pull and waste storage. After sample introduction, the user slides the slider within the housing from one station to another to carry out various unit operations. The slider motion induces blisters to discharge their contents, effectuating washes, and eventual elution of captured nucleic acids into reaction chambers. The slider cassette mates with a processor that incubates isothermal amplification but can also be made to operate instrumentation-free. We demonstrate our cassette’s utility for the co-detection of the human immunodeficiency virus (HIV), hepatitis B virus (HBV), and hepatitis C virus (HCV). These three blood-borne pathogens co-infect many people worldwide with severe personal and public health consequences.

Shifts of SARS-CoV-2 exposure settings in the transmission clusters of 2 epidemic waves in Hong Kong

ABSTRACT Exposure setting is crucial in the formation and propagation of SARS-CoV-2 transmission clusters. In this cohort study, transmission networks of 2 waves were differentiated by exposure setting using territory-wide surveillance data with clinical and laboratory records in Hong Kong. Characteristically, the first wave had resulted from imported cases followed by local transmissions, while the second wave was constituted primarily by local infections. With a 4-fold higher caseload, the second wave featured predominance of epidemiologically linked, local, older and asymptomatic patients with higher viral loads and shorter inpatient days. The 1028 transmission clusters formed 155 cascades composing at least two clusters. Daily and social activities were exposure settings that bridged clusters while residences usually terminated transmission cascades. Regulatory restrictions on social activities extinguished cluster formation in bars, but shifted to private parties in the second wave. The results confirmed that strategic interventions targeting exposure settings could achieve effective epidemic control.

Global control of COVID-19: good vaccines may not suffice

ABSTRACTThe COVID-19 pandemic has unveiled health and socioeconomic inequities around the globe. Effective epidemic control requires the achievement of herd immunity, where susceptible individuals are conferred indirect protection by being surrounded by immunized individuals. The proportion of people that need to be vaccinated to obtain herd immunity is determined through the herd immunity threshold. However, the number of susceptible individuals and the opportunities for contact between infectious and susceptible individuals influence the progress of an epidemic. Thus, in addition to vaccination, control of a pandemic may be difficult or impossible to achieve without other public health measures, including wearing face masks and social distancing. This article discusses the factors that may contribute to herd immunity and control of COVID-19 through the availability of effective vaccines and describes how vaccine effectiveness in the community may be lower than that expected. It also discusses how pandemic control in some countries and populations may face vaccine accessibility barriers if market forces strongly regulate the new technologies available, according to the inverse care law.

Spatial Tessellation of Infectious Disease Spread for Epidemic Decision Support.

Infectious diseases such as COVID-19 have severe impacts on both economy and public health in the US and the world. Due to the heterogeneity of virus spread, there are spatial variations in the demand for medical resources such as personal protective equipment (PPE), testing kits, and vaccines. The availability of such medical resources is critical to effective epidemic control. Although these resources can be readily transported to designated areas for fighting an epidemic, the demand is increasing and varying in space that places significant stress on the supply and allocation of medical resources. However, little has been done on the tessellation of infection distributions for resource management. In this letter, we develop new tessellation algorithms for decision support in epidemic resource allocation and management. The objective is to estimate resource locations and coverage based on the spatial analysis of heterogeneous infection distribution. First, spatial tessellation centroids are initialized through either greedy or cluster-centric approaches. Next, the locations of tessellation centroids are calibrated through a gradient learning algorithm. Lastly, the spread tessellation is computed to provide an estimation of resource coverages under the heterogeneous infection distribution. The proposed methodology is evaluated and validated using a COVID-19 case study of infection data in Pennsylvania. Experimental results show the proposed methodology effectively tessellates the spread of infectious diseases. The new spread tessellation algorithms are shown to have strong potentials for epidemic decision support in infection modelling and resource allocation.

Detection of Dengue Fever Nonstructural Protein 1 Antigen by Proteolytic Peptide Imprinting Technology and UHPLC-MS/MS.

Dengue fever is caused by mosquito-transmitted dengue virus infection and continues to increase worldwide, threatening public health in tropical and subtropical regions. The primary difficulties in preventing a reduction of the medical burden of dengue fever lies in the lack of effective mosquito control, preventive dengue vaccines, and clinically effective antiviral drugs to treat dengue infections. Rapid and accurate diagnosis is crucial for proper patient care and effective control of epidemics. The present work proposes an alternative strategy for detecting the dengue virus nonstructural protein 1 (NS1) antigen in clinical serum samples by using ultrahigh-performance liquid chromatography tandem mass spectrometry (UHPLC-MS/MS) in combination with the molecularly imprinted polymers. Rather than the whole protein, the NS1 signature peptide is selected as a template for molecular imprinting and quantified as a stoichiometric readout of NS1. Three functional monomers with hydrophobic, positively charged, and negatively charged groups were synthesized by click reactions in terms of the signature peptide. These three functional monomers provide abundant recognition sites for the peptide, allowing the peptide template to be effectively imprinted during polymerization. The imprinting conditions were optimized, and the molecularly imprinted polymers were characterized and used for enriching the signature peptide from digested serum samples by solid-phase extraction and then detected by UHPLC-MS/MS. The proposed method is used to detect the dengue virus NS1 in clinical samples and holds significant promise for early confirmation of dengue virus infection.

Phylogenetic Networks and Parameters Inferred from HIV Nucleotide Sequences of High-Risk and General Population Groups in Uganda: Implications for Epidemic Control.

Phylogenetic inference is useful in characterising HIV transmission networks and assessing where prevention is likely to have the greatest impact. However, estimating parameters that influence the network structure is still scarce, but important in evaluating determinants of HIV spread. We analyzed 2017 HIV pol sequences (728 Lake Victoria fisherfolk communities (FFCs), 592 female sex workers (FSWs) and 697 general population (GP)) to identify transmission networks on Maximum Likelihood (ML) phylogenetic trees and refined them using time-resolved phylogenies. Network generative models were fitted to the observed degree distributions and network parameters, and corrected Akaike Information Criteria and Bayesian Information Criteria values were estimated. 347 (17.2%) HIV sequences were linked on ML trees (maximum genetic distance ≤4.5%, ≥95% bootstrap support) and, of these, 303 (86.7%) that consisted of pure A1 (n = 168) and D (n = 135) subtypes were analyzed in BEAST v1.8.4. The majority of networks (at least 40%) were found at a time depth of ≤5 years. The waring and yule models fitted best networks of FFCs and FSWs respectively while the negative binomial model fitted best networks in the GP. The network structure in the HIV-hyperendemic FFCs is likely to be scale-free and shaped by preferential attachment, in contrast to the GP. The findings support the targeting of interventions for FFCs in a timely manner for effective epidemic control. Interventions ought to be tailored according to the dynamics of the HIV epidemic in the target population and understanding the network structure is critical in ensuring the success of HIV prevention programs.

Early Detection of Dengue Fever Outbreaks Using a Surveillance App (Mozzify): Cross-sectional Mixed Methods Usability Study.

BackgroundWhile early detection and effective control of epidemics depend on appropriate surveillance methods, the Philippines bases its dengue fever surveillance system on a passive surveillance method (notifications from barangay/village health centers, municipal or city health offices, hospitals, and clinics). There is no available mHealth (mobile health) app for dengue fever that includes all the appropriate surveillance methods in early detection of disease outbreaks in the country.ObjectiveThis study aimed to evaluate the usability of the Mozzify app in terms of objective quality (engagement, functionality, aesthetics, information) and app subjective and app-specific qualities and compare total app mean score ratings by sociodemographic profile and self and family dengue fever history to see what factors are associated with high app mean score rating among school-based young adult samples and health care professionals. Individual interviews and focus group discussions were also conducted among participants to develop themes from their comments and suggestions to help structure further improvement and future development of the app.MethodsUser experience sessions were conducted among participants, and the Mobile Application Rating Scale (MARS) professional and user versions (uMARS) were administered followed by individual interviews and focus group discussions. Descriptive statistical analysis of the MARS and uMARS score ratings was performed. The total app mean score ratings by sociodemographic and dengue fever history using nonparametric mean difference analyses were also conducted. Thematic synthesis was used to develop themes from the comments and suggestions raised in individual interviews and focus group discussions.ResultsMozzify obtained an overall >4 (out of 5) mean score ratings in the MARS and uMARS app objective quality (4.45), subjective (4.17), and specific (4.55) scales among 948 participants (79 health care professionals and 869 school-based samples). Mean difference analyses revealed that total app mean score ratings were not significantly different across ages and gender among health care professionals and across age, income categories, and self and family dengue fever history but not gender (P<.001) among the school-based samples. Thematic syntheses revealed 7 major themes: multilanguage options and including other diseases; Android version availability; improvements on the app’s content, design, and engagement; inclusion of users from low-income and rural areas; Wi-Fi connection and app size concerns; data credibility and issues regarding user security and privacy.ConclusionsWith its acceptable performance as perceived by health care professionals and school-based young adults, Mozzify has the potential to be used as a strategic health intervention system for early detection of disease outbreaks in the Philippines. It can be used by health care professionals of any age and gender and by school-based samples of any age, socioeconomic status, and dengue fever history. The study also highlights the feasibility of school-based young adults to use health-related apps for disease prevention.

The influence of interpersonal relationships on school adaptation among Chinese university students during COVID-19 control period: Multiple mediating roles of social support and resilience

BackgroundOwing to the government's effective epidemic control measures, universities in some areas of China gradually resumed offline teaching six months after the COVID-19 outbreak. Although attention should now be paid to the experiences of students after they returned to campus, few studies have explored the factors and mechanisms that have influenced these students’ school adaptation. The present study investigated the multiple roles of social support and resilience in mediating associations the relationship between Chinese university students’ interpersonal relationships and their school adaptation during COVID-19 control period. MethodsA cross-sectional survey was conducted with 4534 Chinese university students (Mage = 19.70, SD = 1.14) at two universities in Jiangxi provinces. The independent variable was interpersonal relationships; mediating variables were social support and resilience; and the dependent variable was school adaptation. Multiple mediation analysis was performed using the MPlus software. ResultsControlling for demographic variables, the quality of students’ interpersonal relationships was significantly and positively related to their school adaptation, with students’ ratings of social support and resilience mediating these associations. More interestingly, social support and resilience played multiple mediating roles in the relationship between interpersonal relationships and school adaptation. LimitationsThe age stage of the sample and the methods in which the data were collected may affect the generalizability of the results. ConclusionsDuring COVID-19 control period, interpersonal relationships can influence school adaptation either directly or indirectly by enhancing social support or resilience (parallel mediation) or by activating resilience via the experience of social support (serial mediation). This study's results emphasize the role of interpersonal relationships, as well as the contributions of positive external and internal factors on students’ school adaptation during the epidemic control period. Accordingly, these findings may have implications for the mental health education of college students in the post-epidemic era.

A New Tech-Powered Epidemic Control Versus Flattening the Curve: Insights from South Korea's Experience in COVID-19 Control

Background: As a new approach to control COVID-19, we have conceptualised a tech-powered model that pre-empts further infection by comprehensively identifying the infection’s sources and traces. This model aims to quickly cap the epidemic’s curve instead of traditional flattening. We have documented empirical evidence for this new conceptualization.Methods: Data regarding daily incidences and fatalities from the outbreak to 12 August 2020 were collected from nine countries that had reported their outbreak before 01 February 2020. Each country’s epidemic curve was characterised by its peak height (peak incidence), time to peak (TTP), and cumulative incidence (CI). It was hypothesised that a quicker TTP is associated with a lower peak incidence, lower CI, and lower case-fatality when the epidemic curves are capped instead of being flattened.Results: Since the outbreak, China and South Korea have had the two quickest TTPs of 40·70 and 45·37 days, respectively, and the lowest peak incidences of 2·95 and 4·65 cases per day respectively. Meanwhile, Sweden known for its laissez-faire approach to dealing with the epidemic had the longest TTP (120·36 days) and the highest peak incidence (279·74 days). Quicker TTPs were correlated with lower peak incidences (ρ = 0·896, p = 0·0026) and with lower CIs (0·864, p = 0·0056), which supports the hypothesis.Conclusions: Overall, the multiple nations concerned had implemented a tech-powered approach to cap the epidemic curve. The conceptualisation of the capping model marks a paradigm shift in epidemic control and accordingly informs the design of a more effective epidemic control program.Funding Statement: This study did not receive any funding.Declaration of Interests: All the authors of this study declare that they have no conflict of interest.

Infectious or Recovered? Optimizing the Infectious Disease Detection Process for Epidemic Control and Prevention Based on Social Media.

Detecting the period of a disease is of great importance to building information management capacity in disease control and prevention. This paper aims to optimize the disease surveillance process by further identifying the infectious or recovered period of flu cases through social media. Specifically, this paper explores the potential of using public sentiment to detect flu periods at word level. At text level, we constructed a deep learning method to classify the flu period and improve the classification result with sentiment polarity. Three important findings are revealed. Firstly, bloggers in different periods express significantly different sentiments. Blogger sentiments in the recovered period are more positive than in the infectious period when measured by the interclass distance. Secondly, the optimized disease detection process can substantially improve the classification accuracy of flu periods from 0.876 to 0.926. Thirdly, our experimental results confirm that sentiment classification plays a crucial role in accuracy improvement. Precise identification of disease periods enhances the channels for the disease surveillance processes. Therefore, a disease outbreak can be predicted credibly when a larger population is monitored. The research method proposed in our work also provides decision making reference for proactive and effective epidemic control and prevention in real time.

Quarantine and Isolation: an Outdated Intervention or an Effective Epidemic Control Measure

This article is a Letter to the Editor and does not include an Abstract.

A balance act: minimizing economic loss while controlling novel coronavirus pneumonia

The outbreak of Novel Coronavirus Pneumonia (NCP) has significantly affected China and beyond. How to effectively control such epidemic has gradually become a global issue. This paper reviews the economic impact of major epidemics such as SARS, H1N1, and Ebola at the micro-, sector-, and macro-level. The challenge of effective epidemic control is to achieve a balance between viral transmission reduction and economic cost. This paper then summarizes three main methods to evaluate the effectiveness of several control policies. We also find that the adequacy and authenticity of information disclosure is of great importance to minimize economic loss, as either public panic due to overestimation or lack of public awareness due to underestimation can cause additional negative economic impacts. Accurate and transparent disclosure of information plays a crucial role associated with fighting against the epidemic. Finally, the paper puts forward a number of policy recommendations to minimize economic loss while controlling the spread of COVID-19.

Women and HIV: Addressing Contemporary Issues

In 2018, 18.8 million women were living with human immunodeficiency virus (HIV) and accounted for 52% of persons living with HIV worldwide. Despite exceptional gains made in reducing the HIV burden, women remain underrepresented and underserved. As we move toward ending the global HIV epidemic, key issues which impact women’s risk for acquiring HIV and the barriers which may prevent effective epidemic control in women will have to be addressed. The risks of women as receptive sex partners, the scarcity of HIV prevention options for women and pregnancy concerns require specific attention. Additionally, as the projected life-spans of women living with HIV increases, our focus must also expand to those issues specific to the aging woman. We must aim to reduce comorbidities and address unintended medication induced effects. To reach and sustain global control of HIV, psychological well-being and the quality of life of women living with HIV should be prioritized. This report investigates a few of the biological, psychological and scientific issues surrounding women which challenge the feasibility and sustainability of an end to the HIV epidemic.