Multiple outbreaks for the same pandemic: Local transportation and social distancing explain the different "waves" of A-H1N1pdm cases observed in México during 2009

Social Distancing in the COVID-19 Pandemic: Associated Factors, Health Outcomes, and Implications

Social distancing has reemerged as a public health measure for containing the spread of COVID-19. This integrative review aims to analyze the historical use of social distancing, the current application during COVID-19, individual factors that affect social distancing practices, and consequential health outcomes. We analyzed relevant literature from searches conducted on Scopus, PubMed, and PsycINFO. We found that resources, culture, age, gender, and personality are associated with the degree to which people practice social distancing. Furthermore, social distancing changes our lifestyles and behavior and results in multifaceted health outcomes, including decreased physical activity and sunlight exposure, increased weight gain, and impaired sleep quality. On the positive side, social distancing has been linked to reduced crime rates and environmental damage, as well as better social and family ties. Future interventions may be utilized to increase adherence to social distancing practices and to mitigate the negative health effects of social distancing.

Inference on a Multi-Patch Epidemic Model with Partial Mobility, Residency, and Demography: Case of the 2020 COVID-19 Outbreak in Hermosillo, Mexico

Most studies modeling population mobility and the spread of infectious diseases, particularly those using meta-population multi-patch models, tend to focus on the theoretical properties and numerical simulation of such models. As such, there is relatively scant literature focused on numerical fit, inference, and uncertainty quantification of epidemic models with population mobility. In this research, we use three estimation techniques to solve an inverse problem and quantify its uncertainty for a human-mobility-based multi-patch epidemic model using mobile phone sensing data and confirmed COVID-19-positive cases in Hermosillo, Mexico. First, we utilize a Brownian bridge model using mobile phone GPS data to estimate the residence and mobility parameters of the epidemic model. In the second step, we estimate the optimal model epidemiological parameters by deterministically inverting the model using a Darwinian-inspired evolutionary algorithm (EA)-that is, a genetic algorithm (GA). The third part of the analysis involves performing inference and uncertainty quantification in the epidemic model using two Bayesian Monte Carlo sampling methods: t-walk and Hamiltonian Monte Carlo (HMC). The results demonstrate that the estimated model parameters and incidence adequately fit the observed daily COVID-19 incidence in Hermosillo. Moreover, the estimated parameters from the HMC method yield large credible intervals, improving their coverage for the observed and predicted daily incidences. Furthermore, we observe that the use of a multi-patch model with mobility yields improved predictions when compared to a single-patch model.

Investigating the relationships between concentrated disadvantage, place connectivity, and COVID-19 fatality in the United States over time

BACKGROUND

Concentrated disadvantaged areas have been disproportionately affected by COVID-19 outbreak in the United States (US). Meanwhile, highly connected areas may contribute to higher human movement, leading to higher COVID-19 cases and deaths. This study examined the associations between concentrated disadvantage, place connectivity, and COVID-19 fatality in the US over time.

METHODS

Concentrated disadvantage was assessed based on the spatial concentration of residents with low socioeconomic status. Place connectivity was defined as the normalized number of shared Twitter users between the county and all other counties in the contiguous US in a year (Y = 2019). COVID-19 fatality was measured as the cumulative COVID-19 deaths divided by the cumulative COVID-19 cases. Using county-level (N = 3,091) COVID-19 fatality over four time periods (up to October 31, 2021), we performed mixed-effect negative binomial regressions to examine the association between concentrated disadvantage, place connectivity, and COVID-19 fatality, considering potential state-level variations. The moderation effects of county-level place connectivity and concentrated disadvantage were analyzed. Spatially lagged variables of COVID-19 fatality were added to the models to control for the effect of spatial autocorrelations in COVID-19 fatality.

RESULTS

Concentrated disadvantage was significantly associated with an increased COVID-19 fatality in four time periods (p < 0.01). More importantly, moderation analysis suggested that place connectivity significantly exacerbated the harmful effect of concentrated disadvantage on COVID-19 fatality in three periods (p < 0.01), and this significant moderation effect increased over time. The moderation effects were also significant when using place connectivity data from the previous year.

CONCLUSIONS

Populations living in counties with both high concentrated disadvantage and high place connectivity may be at risk of a higher COVID-19 fatality. Greater COVID-19 fatality that occurs in concentrated disadvantaged counties may be partially due to higher human movement through place connectivity. In response to COVID-19 and other future infectious disease outbreaks, policymakers are encouraged to take advantage of historical disadvantage and place connectivity data in epidemic monitoring and surveillance of the disadvantaged areas that are highly connected, as well as targeting vulnerable populations and communities for additional intervention.

Using WhatsApp During the COVID-19 Pandemic and the Emotions and Perceptions of Users

Background

Purpose

Methods and Participants

Results

Conclusion

A linear dynamical perspective on epidemiology: interplay between early COVID-19 outbreak and human mobility

This paper investigates the impact of human activity and mobility (HAM) in the spreading dynamics of an epidemic. Specifically, it explores the interconnections between HAM and its effect on the early spread of the COVID-19 virus. During the early stages of the pandemic, effective reproduction numbers exhibited a high correlation with human mobility patterns, leading to a hypothesis that the HAM system can be studied as a coupled system with disease spread dynamics. This study applies the generalized Koopman framework with control inputs to determine the nonlinear disease spread dynamics and the input-output characteristics as a locally linear controlled dynamical system. The approach solely relies on the snapshots of spatiotemporal data and does not require any knowledge of the system's underlying physical laws. We exploit the Koopman operator framework by utilizing the Hankel dynamic mode decomposition with Control (HDMDc) algorithm to obtain a linear disease spread model incorporating human mobility as a control input. The study demonstrated that the proposed methodology could capture the impact of local mobility on the early dynamics of the ongoing global pandemic. The obtained locally linear model can accurately forecast the number of new infections for various prediction windows ranging from two to four weeks. The study corroborates a leader-follower relationship between mobility and disease spread dynamics. In addition, the effect of delay embedding in the HDMDc algorithm is also investigated and reported. A case study was performed using COVID infection data from Florida, US, and HAM data extracted from Google community mobility data report.

Cognitive factors influenced physical distancing adherence during the COVID-19 pandemic in a population-specific way

Even early in the COVID-19 pandemic, adherence to physical distancing measures was variable, exposing some communities to elevated risk. While cognitive factors from the Health Belief Model (HBM) and resilience correlate with compliance with physical distancing, external conditions may preclude full compliance with physical distancing guidelines. Our objective was to identify HBM and resilience constructs that could be used to improve adherence to physical distancing even when full compliance is not possible. We examined adherence as expressed through 7-day non-work, non-household contact rates in two cohorts: 1) adults in households with children from Minnesota and Iowa; and 2) adults ≥50 years-old from Minnesota, one-third of whom had Parkinson's disease. We identified multiple cognitive factors associated with physical distancing adherence, specifically perceived severity, benefits, self-efficacy, and barriers. However, the magnitude, and occasionally the direction, of these associations was population-dependent. In Cohort 1, perceived self-efficacy for remaining 6-feet from others was associated with a 29% lower contact rate (RR 0.71; 95% CI 0.65, 0.77). This finding was consistent across all race/ethnicity and income groups we examined. The barriers to adherence of having a child in childcare and having financial concerns had the largest effects among individuals from marginalized racial and ethnic groups and high-income households. In Cohort 2, self-efficacy to quarantine/isolate was associated with a 23% decrease in contacts (RR 0.77; 95% CI 0.66, 0.89), but upon stratification by education level, the association was only present for those with at least a Bachelor's degree. Education also modified the effect of the barrier to adherence leaving home for work, increasing contacts among those with a Bachelor's degree and reducing contacts among those without. Our findings suggest that public health messaging tailored to the identified cognitive factors has the potential to improve physical distancing adherence, but population-specific needs must be considered to maximize effectiveness.

Modelling epidemic spread in cities using public transportation as a proxy for generalized mobility trends

We study how public transportation data can inform the modeling of the spread of infectious diseases based on SIR dynamics. We present a model where public transportation data is used as an indicator of broader mobility patterns within a city, including the use of private transportation, walking etc. The mobility parameter derived from this data is used to model the infection rate. As a test case, we study the impact of the usage of the New York City subway on the spread of COVID-19 within the city during 2020. We show that utilizing subway transport data as an indicator of the general mobility trends within the city, and therefore as an indicator of the effective infection rate, improves the quality of forecasting COVID-19 spread in New York City. Our model predicts the two peaks in the spread of COVID-19 cases in NYC in 2020, unlike a standard SIR model that misses the second peak entirely.

Pandemics and Clinical Psychology

Pandemics are complex events involving a range of stressors affecting mental health. The recent COVID-19 pandemic served as a catalyst, accelerating preexisting trends in clinical care such as the rise of e-health for rapidly and broadly disseminating psychological services. The process of adapting face-to-face clinical services to online formats occurred rapidly during COVID-19, underscoring the adaptability of clinicians to meet new challenges. However, COVID-19 also highlighted important shortcomings in clinical care, including planning deficiencies and shortages of clinicians with specialized training for treating various psychological problems (e.g., prolonged grief disorder). These problems and potential solutions are discussed.

The Psychology of Pandemics

This article reviews the current state of knowledge and promising new directions concerning the psychology of pandemics. Pandemics are disease outbreaks that spread globally. Historically, psychological factors have been neglected by researchers and health authorities despite evidence that pandemics are, to a large extent, psychological phenomena whereby beliefs and behaviors influence the spreading versus containment of infection. Psychological factors are important in determining (a) adherence to pandemic mitigation methods (e.g., adherence to social distancing), (b) pandemic-related social disruption (e.g., panic buying, racism, antilockdown protests), and (c) pandemic-related distress and related problems (e.g., anxiety, depression, posttraumatic stress disorder, prolonged grief disorder). The psychology of pandemics has emerged as an important field of research and practice during the coronavirus 2019 (COVID-19) pandemic. As a scholarly discipline, the psychology of pandemics is fragmented and diverse, encompassing various psychological subspecialties and allied disciplines, but is vital for shaping clinical practice and public health guidelines for COVID-19 and future pandemics. Expected final online publication date for the Annual Review of Clinical Psychology, Volume 18 is May 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Developmental Understanding of Death and Grief Among Children During COVID-19 Pandemic: Application of Bronfenbrenner's Bioecological Model

COVID-19 Pandemic has influenced death-related attitudes and understanding during the childhood development leading to a life-long impact. Factors like pandemic-related movement restrictions, school closures, and parents' stay-at-home have exposed children to the phenomenon of grief and death. In that case, children anticipate adverse outcomes and fear while they struggle with unanswered questions. Children may not have coping skills needed to manage their grief in constructive ways to identify, normalize, and express their responses to the loss in their lives. Naming and validating these responses as distinctive aspects of grief process and providing safe space to express their feelings are essential components of a child's coping with loss and grief. This is crucial to consider, as different children react to and are influenced by their environments differently. This article aims to explore the developmental understanding of the process of death and grief by applying the conceptual framework of Bronfenbrenner's theory. Understanding mutual interaction between a child and various ecological systems determines how children perceive death and process grief can facilitate effective communication that has significant implications.

Effectiveness of non-pharmaceutical interventions related to social distancing on respiratory viral infectious disease outcomes: A rapid evidence-based review and meta-analysis

OBJECTIVES

Non-pharmaceutical interventions (e.g. quarantine and isolation) are used to mitigate and control viral infectious disease, but their effectiveness has not been well studied. For COVID-19, disease control efforts will rely on non-pharmaceutical interventions until pharmaceutical interventions become widely available, while non-pharmaceutical interventions will be of continued importance thereafter.

METHODS

This rapid evidence-based review provides both qualitative and quantitative analyses of the effectiveness of social distancing non-pharmaceutical interventions on disease outcomes. Literature was retrieved from MEDLINE, Google Scholar, and pre-print databases (BioRxiv.org, MedRxiv.org, and Wellcome Open Research).

RESULTS

Twenty-eight studies met inclusion criteria (n = 28). Early, sustained, and combined application of various non-pharmaceutical interventions could mitigate and control primary outbreaks and prevent more severe secondary or tertiary outbreaks. The strategic use of non-pharmaceutical interventions decreased incidence, transmission, and/or mortality across all interventions examined. The pooled attack rates for no non-pharmaceutical intervention, single non-pharmaceutical interventions, and multiple non-pharmaceutical interventions were 42% (95% confidence interval = 30% - 55%), 29% (95% confidence interval = 23% - 36%), and 22% (95% confidence interval = 16% - 29%), respectively.

CONCLUSION

Implementation of multiple non-pharmaceutical interventions at key decision points for public health could effectively facilitate disease mitigation and suppression until pharmaceutical interventions become available. Dynamics around R 0 values, the susceptibility of certain high-risk patient groups to infection, and the probability of asymptomatic cases spreading disease should be considered.

COVID-19 infection data encode a dynamic reproduction number in response to policy decisions with secondary wave implications

The SARS-CoV-2 virus is responsible for the novel coronavirus disease 2019 (COVID-19), which has spread to populations throughout the continental United States. Most state and local governments have adopted some level of “social distancing” policy, but infections have continued to spread despite these efforts. Absent a vaccine, authorities have few other tools by which to mitigate further spread of the virus. This begs the question of how effective social policy really is at reducing new infections that, left alone, could potentially overwhelm the existing hospitalization capacity of many states. We developed a mathematical model that captures correlations between some state-level “social distancing” policies and infection kinetics for all U.S. states, and use it to illustrate the link between social policy decisions, disease dynamics, and an effective reproduction number that changes over time, for case studies of Massachusetts, New Jersey, and Washington states. In general, our findings indicate that the potential for second waves of infection, which result after reopening states without an increase to immunity, can be mitigated by a return of social distancing policies as soon as possible after the waves are detected.

Social distancing and testing as optimal strategies against the spread of COVID-19 in the Rio Grande Valley of Texas

At the beginning of August 2020, the Rio Grande Valley (RGV) of Texas experienced a rapid increase of coronavirus disease 2019 (abbreviated as COVID-19) cases and deaths. This study aims to determine the optimal levels of effective social distancing and testing to slow the virus spread at the outset of the pandemic. We use an age-stratified eight compartment epidemiological model to depict COVID-19 transmission in the community and within households. With a simulated 120-day outbreak period data we obtain a post 180-days period optimal control strategy solution. Our results show that easing social distancing between adults by the end of the 180-day period requires very strict testing a month later and then daily testing rates of 5% followed by isolation of positive cases. Relaxing social distancing rates in adults from 50% to 25% requires both children and seniors to maintain social distancing rates of 50% for nearly the entire period while maintaining maximum testing rates of children and seniors for 150 of the 180 days considered in this model. Children have higher contact rates which leads to transmission based on our model, emphasizing the need for caution when considering school reopenings.

Consequences of school closure on access to education: Lessons from the 2013-2016 Ebola pandemic

The COVID-19 pandemic has seen an unprecedented shutdown of society. Among the various safety measures taken, much attention has been given to school closure as a non-pharmaceutical mitigation tool to curb the spread of the disease through ensuring "social" (physical) distancing. Nearly 1.725 billion children in over 95% of countries worldwide have been affected by school closures implemented in April 2020 as the virus continued to spread. In the field of education, policymakers' attention has been directed at keeping students on board through remote learning and addressing the immediate needs of schools upon reopening. The study presented in this article focuses on who remains absent after schools resume. Using publicly available survey data from the USAID Demographic Health Surveys Program and the UNICEF Multiple Indicator Cluster Survey from before and after the 2013-2016 Ebola pandemic in Guinea and Sierra Leone in West Africa, the author examined changes in school enrolment and dropout patterns, with targeted consideration given to traditionally marginalised groups. At the time, schools closed for between seven to nine months in the two countries; this length and intensity makes this Ebola pandemic the only health crisis in the recent past to come close to the pandemic-related school closures experienced in 2020. The author's findings suggest that post-Ebola, youth in the poorest households saw the largest increase in school dropout. Exceeding expected pre-Ebola dropout rates, an additional 17,400 of the poorest secondary-age youth were out of school. This evidence is important for minimising the likely post-COVID-19 expansion in inequality. The author's findings point to the need for sustainable planning that looks beyond the reopening of educational institutions to include comprehensive financial support packages for groups most likely to be affected.

Observed impacts of the Covid-19 first wave on travel behaviour in Switzerland based on a large GPS panel

In Switzerland, strict measures as a response to the Covid-19 pandemic were imposed on March 16, 2020, before being gradually relaxed from May 11 onwards. We report the impact of these measures on mobility behaviour based on a GPS tracking panel of 1439 Swiss residents. The participants were also exposed to online questionnaires. The impact of both the lockdown and the relaxation of the measures up until the middle of August 2020 are presented. Reductions of around 60% in the average daily distance were observed, with decreases of over 90% for public transport. Cycling increased in mode share drastically. Behavioural shifts can even be observed in response to the announcement of the measures and relaxation, a week before they came in to place. Long-term implications for policy are discussed, in particular the increased preference for cycling as a result of the pandemic.

Mobile Phone GPS Data and Prevalence of COVID-19 Infections: Quantifying Parameters of Social Distancing in the U.S

BACKGROUND

To evaluate the association between social distancing quantified by mobile phone data and the current prevalence of COVID-19 infections in the U.S. per capita.

METHODS

Data were accessed on April 4, 2020, from Centers for Disease Control and Prevention, Google COVID-19 Community Mobility Report, and the United States Census Bureau to report prevalence of COVID-19 infections, mobility data, and population per state, respectively. Mobility data points were defined as daily length of visit or time spent in a single location based on mobile phone users shared locations from February 7 - March 29, 2020. Multivariable linear regression was used to evaluate relationships between normalized per capita infection prevalence and six parameters of social distancing.

RESULTS

Mobility data indicated the following percent changes compared to median values of baseline activity: -50% in transit stations, -45% in retail/recreation, -36% in workplaces, -23% in grocery/pharmacy, -19% in parks, and +12% in residential living areas. Multivariable linear regression revealed significant correlation between prevalence of infection per capita and parameters of social distancing (R= 0.604, P= 0.002). Time at home was not an independent predictor for prevalence of infection per capita (beta= 0.016; 95% CI, -0.003 to 0.036; P= 0.09).

CONCLUSION

Based on mobility reports from mobile phone GPS data and six characteristics of social distancing, significant associations were identified between geographic activity and prevalence of COVID-19 infections in the U.S. per capita. Mobile phone data utilizing 'location history' may be warranted to monitor the effectiveness of social distancing parameters on reducing prevalence of COVID-19 in the U.S.

Community interventions in Low-And Middle-Income Countries to inform COVID-19 control implementation decisions in Kenya: A rapid systematic review

Globally, public health measures like face masks, hand hygiene and maintaining social distancing have been implemented to delay and reduce local transmission of COVID-19. To date there is emerging evidence to provide effectiveness and compliance to intervention measures on COVID-19 due to rapid spread of the disease. We synthesized evidence of community interventions and innovative practices to mitigate COVID-19 as well as previous respiratory outbreak infections which may share some aspects of transmission dynamics with COVID-19. In the study, we systematically searched the literature on community interventions to mitigate COVID-19, SARS (severe acute respiratory syndrome), H1N1 Influenza and MERS (middle east respiratory syndrome) epidemics in PubMed, Google Scholar, World Health Organization (WHO), MEDRXIV and Google from their inception until May 30, 2020 for up-to-date published and grey resources. We screened records, extracted data, and assessed risk of bias in duplicates. We rated the certainty of evidence according to Cochrane methods and the GRADE approach. This study is registered with PROSPERO (CRD42020183064). Of 41,138 papers found, 17 studies met the inclusion criteria in various settings in Low- and Middle-Income Countries (LMICs). One of the papers from LMICs originated from Africa (Madagascar) with the rest from Asia 9 (China 5, Bangladesh 2, Thailand 2); South America 5 (Mexico 3, Peru 2) and Europe 2 (Serbia and Romania). Following five studies on the use of face masks, the risk of contracting SARS and Influenza was reduced OR 0.78 and 95% CI = 0.36–1.67. Equally, six studies on hand hygiene practices reported a reduced risk of contracting SARS and Influenza OR 0.95 and 95% CI = 0.83–1.08. Further two studies that looked at combined use of face masks and hand hygiene interventions showed the effectiveness in controlling the transmission of influenza OR 0.94 and 95% CI = 0.58–1.54. Nine studies on social distancing intervention demonstrated the importance of physical distance through closure of learning institutions on the transmission dynamics of disease. The evidence confirms the use of face masks, good hand hygiene and social distancing as community interventions are effective to control the spread of SARS and influenza in LMICs. However, the effectiveness of community interventions in LMICs should be informed by adherence of the mitigation measures and contextual factors taking into account the best practices. The study has shown gaps in adherence/compliance of the interventions, hence a need for robust intervention studies to better inform the evidence on compliance of the interventions. Nevertheless, this rapid review of currently best available evidence might inform interim guidance on similar respiratory infectious diseases like Covid-19 in Kenya and similar LMIC context.

Transport-related experiences in China in response to the Coronavirus (COVID-19)

In the year 2020, the Coronavirus (COVID-19) broke out in many countries of the world. In China, the Chinese government and people have adopted strong measures. After more than a month of fighting against the epidemic, the epidemic has been basically under control in China. However, in other countries of the world, such as South Korea, Japan, Iran, many countries in Europe, and the Americas, the epidemic is still developing rapidly and the situation is not optimistic. This paper summarizes the transport-related experience of China in the fight against the epidemic and hopes that it will be helpful to other countries in the fight against the epidemic.

Social Distancing Metrics and Estimates of SARS-CoV-2 Transmission Rates: Associations Between Mobile Telephone Data Tracking and R

BACKGROUND

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of coronavirus disease 2019 (COVID-19). In the absence of robust preventive or curative strategies, the implementation of social distancing has been a key component of limiting the spread of the virus.

METHODS

Daily estimates of R(t) were calculated and compared with measures of social distancing made publicly available by Unacast. Daily generated variables representing an overall grade for distancing, changes in distances traveled, encounters between individuals, and daily visitation, were modeled as predictors of average R value for the following week, using linear regression techniques for 8 counties surrounding the city of Syracuse, New York. Supplementary analysis examined differences between counties.

RESULTS

A total of 225 observations were available across the 8 counties, with 166 meeting the mean R(t) < 3 outlier criterion for the regression models. Measurements for distance (β = 1.002, P = .012), visitation (β = .887, P = .017), and encounters (β = 1.070, P = .001) were each predictors of R(t) for the following week. Mean R(t) drops when overall distancing grades move from D+ to C-. These trends were significant (P < .001 for each).

CONCLUSIONS

Social distancing, when assessed by free and publicly available measures such as those shared by Unacast, has an impact on viral transmission rates. The scorecard may also be useful for public messaging about social distance, in hospital planning, and in the interpretation of epidemiological models.

Mechanistic modelling of multiple waves in an influenza epidemic or pandemic

Multiple-wave outbreaks have been documented for influenza pandemics particularly in the temperate zone, and occasionally for seasonal influenza epidemics in the tropical zone. The mechanisms shaping multiple-wave influenza outbreaks are diverse but are yet to be summarized in a systematic fashion. For this purpose, we described 12 distinct mechanistic models, among which five models were proposed for the first time, that support two waves of infection in a single influenza season, and classified them into five categories according to heterogeneities in host, pathogen, space, time and their combinations, respectively. To quantify the number of infection waves, we proposed three metrics that provide robust and intuitive results for real epidemics. Further, we performed sensitivity analyses on key parameters in each model and found that reducing the basic reproduction number or the transmission rate, limiting the addition of susceptible people who are to get the primary infection to infected areas, and limiting the probability of replenishment of people who are to be reinfected in the short term, could decrease the number of infection waves and clinical attack rate. Finally, we introduced a modelling framework to infer the mechanisms driving two-wave outbreaks. A better understanding of two-wave mechanisms could guide public health authorities to develop and implement preparedness plans and deploy control strategies.

Epidemics on networks: Reducing disease transmission using health emergency declarations and peer communication

Understanding individual decisions in a world where communications and information move instantly via cell phones and the internet, contributes to the development and implementation of policies aimed at stopping or ameliorating the spread of diseases. In this manuscript, the role of official social network perturbations generated by public health officials to slow down or stop a disease outbreak are studied over distinct classes of static social networks. The dynamics are stochastic in nature with individuals (nodes) being assigned fixed levels of education or wealth. Nodes may change their epidemiological status from susceptible, to infected and to recovered. Most importantly, it is assumed that when the prevalence reaches a pre-determined threshold level,P * , information, called awareness in our framework, starts to spread, a process triggered by public health authorities. Information is assumed to spread over the same static network and whether or not one becomes a temporary informer, is a function of his/her level of education or wealth and epidemiological status. Stochastic simulations show that threshold selectionP * and the value of the average basic reproduction number impact the final epidemic size differentially. For the Erdős-Rényi and Small-world networks, an optimal choice forP * that minimize the final epidemic size can be identified under some conditions while for Scale-free networks this is not case.

Urbanization affects peak timing, prevalence, and bimodality of influenza pandemics in Australia: Results of a census-calibrated model

We examine salient trends of influenza pandemics in Australia, a rapidly urbanizing nation. To do so, we implement state-of-the-art influenza transmission and progression models within a large-scale stochastic computer simulation, generated using comprehensive Australian census datasets from 2006, 2011, and 2016. Our results offer a simulation-based investigation of a population's sensitivity to pandemics across multiple historical time points and highlight three notable trends in pandemic patterns over the years: increased peak prevalence, faster spreading rates, and decreasing spatiotemporal bimodality. We attribute these pandemic trends to increases in two key quantities indicative of urbanization: the population fraction residing in major cities and international air traffic. In addition, we identify features of the pandemic's geographic spread that we attribute to changes in the commuter mobility network. The generic nature of our model and the ubiquity of urbanization trends around the world make it likely for our results to be applicable in other rapidly urbanizing nations.

Community Mitigation Guidelines to Prevent Pandemic Influenza - United States, 2017

When a novel influenza A virus with pandemic potential emerges, nonpharmaceutical interventions (NPIs) often are the most readily available interventions to help slow transmission of the virus in communities, which is especially important before a pandemic vaccine becomes widely available. NPIs, also known as community mitigation measures, are actions that persons and communities can take to help slow the spread of respiratory virus infections, including seasonal and pandemic influenza viruses.These guidelines replace the 2007 Interim Pre-pandemic Planning Guidance: Community Strategy for Pandemic Influenza Mitigation in the United States - Early, Targeted, Layered Use of Nonpharmaceutical Interventions (https://stacks.cdc.gov/view/cdc/11425). Several elements remain unchanged from the 2007 guidance, which described recommended NPIs and the supporting rationale and key concepts for the use of these interventions during influenza pandemics. NPIs can be phased in, or layered, on the basis of pandemic severity and local transmission patterns over time. Categories of NPIs include personal protective measures for everyday use (e.g., voluntary home isolation of ill persons, respiratory etiquette, and hand hygiene); personal protective measures reserved for influenza pandemics (e.g., voluntary home quarantine of exposed household members and use of face masks in community settings when ill); community measures aimed at increasing social distancing (e.g., school closures and dismissals, social distancing in workplaces, and postponing or cancelling mass gatherings); and environmental measures (e.g., routine cleaning of frequently touched surfaces).Several new elements have been incorporated into the 2017 guidelines. First, to support updated recommendations on the use of NPIs, the latest scientific evidence available since the influenza A (H1N1)pdm09 pandemic has been added. Second, a summary of lessons learned from the 2009 H1N1 pandemic response is presented to underscore the importance of broad and flexible prepandemic planning. Third, a new section on community engagement has been included to highlight that the timely and effective use of NPIs depends on community acceptance and active participation. Fourth, to provide new or updated pandemic assessment and planning tools, the novel influenza virus pandemic intervals tool, the Influenza Risk Assessment Tool, the Pandemic Severity Assessment Framework, and a set of prepandemic planning scenarios are described. Finally, to facilitate implementation of the updated guidelines and to assist states and localities with prepandemic planning and decision-making, this report links to six supplemental prepandemic NPI planning guides for different community settings that are available online (https://www.cdc.gov/nonpharmaceutical-interventions).

Community Mitigation Guidelines to Prevent Pandemic Influenza - United States, 2017

When a novel influenza A virus with pandemic potential emerges, nonpharmaceutical interventions (NPIs) often are the most readily available interventions to help slow transmission of the virus in communities, which is especially important before a pandemic vaccine becomes widely available. NPIs, also known as community mitigation measures, are actions that persons and communities can take to help slow the spread of respiratory virus infections, including seasonal and pandemic influenza viruses.These guidelines replace the 2007 Interim Pre-pandemic Planning Guidance: Community Strategy for Pandemic Influenza Mitigation in the United States - Early, Targeted, Layered Use of Nonpharmaceutical Interventions (https://stacks.cdc.gov/view/cdc/11425). Several elements remain unchanged from the 2007 guidance, which described recommended NPIs and the supporting rationale and key concepts for the use of these interventions during influenza pandemics. NPIs can be phased in, or layered, on the basis of pandemic severity and local transmission patterns over time. Categories of NPIs include personal protective measures for everyday use (e.g., voluntary home isolation of ill persons, respiratory etiquette, and hand hygiene); personal protective measures reserved for influenza pandemics (e.g., voluntary home quarantine of exposed household members and use of face masks in community settings when ill); community measures aimed at increasing social distancing (e.g., school closures and dismissals, social distancing in workplaces, and postponing or cancelling mass gatherings); and environmental measures (e.g., routine cleaning of frequently touched surfaces).Several new elements have been incorporated into the 2017 guidelines. First, to support updated recommendations on the use of NPIs, the latest scientific evidence available since the influenza A (H1N1)pdm09 pandemic has been added. Second, a summary of lessons learned from the 2009 H1N1 pandemic response is presented to underscore the importance of broad and flexible prepandemic planning. Third, a new section on community engagement has been included to highlight that the timely and effective use of NPIs depends on community acceptance and active participation. Fourth, to provide new or updated pandemic assessment and planning tools, the novel influenza virus pandemic intervals tool, the Influenza Risk Assessment Tool, the Pandemic Severity Assessment Framework, and a set of prepandemic planning scenarios are described. Finally, to facilitate implementation of the updated guidelines and to assist states and localities with prepandemic planning and decision-making, this report links to six supplemental prepandemic NPI planning guides for different community settings that are available online (https://www.cdc.gov/nonpharmaceutical-interventions).

Analysis of spatial mobility in subjects from a Dengue endemic urban locality in Morelos State, Mexico

INTRODUCTION

Mathematical models and field data suggest that human mobility is an important driver for Dengue virus transmission. Nonetheless little is known on this matter due the lack of instruments for precise mobility quantification and study design difficulties.

MATERIALS AND METHODS

We carried out a cohort-nested, case-control study with 126 individuals (42 cases, 42 intradomestic controls and 42 population controls) with the goal of describing human mobility patterns of recently Dengue virus-infected subjects, and comparing them with those of non-infected subjects living in an urban endemic locality. Mobility was quantified using a GPS-data logger registering waypoints at 60-second intervals for a minimum of 15 natural days.

RESULTS

Although absolute displacement was highly biased towards the intradomestic and peridomestic areas, occasional displacements exceeding a 100-Km radius from the center of the studied locality were recorded for all three study groups and individual displacements were recorded traveling across six states from central Mexico. Additionally, cases had a larger number of visits out of the municipality´s administrative limits when compared to intradomestic controls (cases: 10.4 versus intradomestic controls: 2.9, p = 0.0282). We were able to identify extradomestic places within and out of the locality that were independently visited by apparently non-related infected subjects, consistent with houses, working and leisure places.

CONCLUSIONS

Results of this study show that human mobility in a small urban setting exceeded that considered by local health authority's administrative limits, and was different between recently infected and non-infected subjects living in the same household. These observations provide important insights about the role that human mobility may have in Dengue virus transmission and persistence across endemic geographic areas that need to be taken into account when planning preventive and control measures. Finally, these results are a valuable reference when setting the parameters for future mathematical modeling studies.

Perspectives on the role of mobility, behavior, and time scales in the spread of diseases

The dynamics, control, and evolution of communicable and vector-borne diseases are intimately connected to the joint dynamics of epidemiological, behavioral, and mobility processes that operate across multiple spatial, temporal, and organizational scales. The identification of a theoretical explanatory framework that accounts for the pattern regularity exhibited by a large number of host-parasite systems, including those sustained by host-vector epidemiological dynamics, is but one of the challenges facing the coevolving fields of computational, evolutionary, and theoretical epidemiology. Host-parasite epidemiological patterns, including epidemic outbreaks and endemic recurrent dynamics, are characteristic to well-identified regions of the world; the result of processes and constraints such as strain competition, host and vector mobility, and population structure operating over multiple scales in response to recurrent disturbances (like El Niño) and climatological and environmental perturbations over thousands of years. It is therefore important to identify and quantify the processes responsible for observed epidemiological macroscopic patterns: the result of individual interactions in changing social and ecological landscapes. In this perspective, we touch on some of the issues calling for the identification of an encompassing theoretical explanatory framework by identifying some of the limitations of existing theory, in the context of particular epidemiological systems. Fostering the reenergizing of research that aims at disentangling the role of epidemiological and socioeconomic forces on disease dynamics, better understood as complex adaptive systems, is a key aim of this perspective.

Role of short-term dispersal on the dynamics of Zika virus in an extreme idealized environment

In November 2015, El Salvador reported their first case of Zika virus (ZIKV) infection, an event followed by an explosive outbreak that generated over 6000 suspected cases in a period of two months. National agencies began implementing control measures that included vector control and recommending an increased use of repellents. Further, in response to the alarming and growing number of microcephaly cases in Brazil, the importance of avoiding pregnancies for two years was stressed. In this paper, we explore the role of mobility within communities characterized by extreme poverty, crime and violence. Specifically, the role of short term mobility between two idealized interconnected highly distinct communities is explored in the context of ZIKV outbreaks. We make use of a Lagrangian modeling approach within a two-patch setting in order to highlight the possible effects that short-term mobility, within highly distinct environments, may have on the dynamics of ZIKV outbreak when the overall goal is to reduce the number of cases not just in the most affluent areas but everywhere. Outcomes depend on existing mobility patterns, levels of disease risk, and the ability of federal or state public health services to invest in resource limited areas, particularly in those where violence is systemic. The results of simulations in highly polarized and simplified scenarios are used to assess the role of mobility. It quickly became evident that matching observed patterns of ZIKV outbreaks could not be captured without incorporating increasing levels of heterogeneity. The number of distinct patches and variations on patch connectivity structure required to match ZIKV patterns could not be met within the highly aggregated model that is used in the simulations.

Impact of School Cycles and Environmental Forcing on the Timing of Pandemic Influenza Activity in Mexican States, May-December 2009

While a relationship between environmental forcing and influenza transmission has been established in inter-pandemic seasons, the drivers of pandemic influenza remain debated. In particular, school effects may predominate in pandemic seasons marked by an atypical concentration of cases among children. For the 2009 A/H1N1 pandemic, Mexico is a particularly interesting case study due to its broad geographic extent encompassing temperate and tropical regions, well-documented regional variation in the occurrence of pandemic outbreaks, and coincidence of several school breaks during the pandemic period. Here we fit a series of transmission models to daily laboratory-confirmed influenza data in 32 Mexican states using MCMC approaches, considering a meta-population framework or the absence of spatial coupling between states. We use these models to explore the effect of environmental, school-related and travel factors on the generation of spatially-heterogeneous pandemic waves. We find that the spatial structure of the pandemic is best understood by the interplay between regional differences in specific humidity (explaining the occurrence of pandemic activity towards the end of the school term in late May-June 2009 in more humid southeastern states), school vacations (preventing influenza transmission during July-August in all states), and regional differences in residual susceptibility (resulting in large outbreaks in early fall 2009 in central and northern Mexico that had yet to experience fully-developed outbreaks). Our results are in line with the concept that very high levels of specific humidity, as present during summer in southeastern Mexico, favor influenza transmission, and that school cycles are a strong determinant of pandemic wave timing.

A spatial-temporal transmission model and early intervention policies of 2009 A/H1N1 influenza in South Korea

We developed a spatial-temporal model of the 2009 A/H1N1 influenza pandemic in the Seoul metropolitan area (SMA), which is located in the north-west of South Korea and is the second-most complex metropolitan area worldwide. This multi-patch influenza model consists of a SEIAR influenza transmission model and flow model between two districts. This model is based on the daily confirmed cases of A/H1N1 influenza collected by the Korea Center for Disease Control and Prevention from April 27 to September 15, 2009 and the daily commuting data from 33 districts of SMA reported in the 2010 Population and Housing Census (PHC). We analyzed the spread patterns of 2009 influenza in the SMA by the reproductive numbers and geographic information systems. During the early period of novel influenza pandemics, when pharmaceutical interventions are lacking, non-pharmaceutical public health interventions will be the most critical strategies for impeding the spread of influenza and delaying an epidemic. Using the spatial-temporal model developed herein, we also investigated the impact of non-pharmaceutical public health interventions, isolation and/or commuting restrictions, on the incidence reduction in various scenarios. Our model provides scientific evidence for predicting the spread of disease and preparedness for a future pandemic.

The role of residence times in two-patch dengue transmission dynamics and optimal strategies

The reemergence and geographical dispersal of vector-borne diseases challenge global health experts around the world and in particular, dengue poses increasing difficulties in the Americas, due in part to explosive urban and semi-urban growth, increases of within and between region mobility, the absence of a vaccine, and the limited resources available for public health services. In this work, a simple deterministic two-patch model is introduced to assess the impact of dengue transmission dynamics in heterogeneous environments. The two-patch system models the movement (e.g. urban versus rural areas residence times) of individuals between and within patches/environments using residence-time matrices with entries that budget within and between host patch relative residence times, under the assumption that only the human budgets their residence time across regions. Three scenarios are considered: (i) resident hosts in Patch i visit patch j, where i≠j but not the other way around, a scenario referred to as unidirectional motion; (ii) symmetric bi-directional motion; and (iii) asymmetric bi-directional motion. Optimal control theory is used to identify and evaluate patch-specific control measures aimed at reducing dengue prevalence in humans and vectors at a minimal cost. Optimal policies are computed under different residence-matrix configurations mentioned above as well as transmissibility scenarios characterized by the magnitude of the basic reproduction number. Optimal patch-specific polices can ameliorate the impact of epidemic outbreaks substantially when the basic reproduction number is moderate. The final patch-specific epidemic size variation increases as the residence time matrix moves away from the symmetric case (asymmetry). As expected, the patch where individuals spend most of their time or in the patch where transmissibility is higher tend to support larger patch-specific final epidemic sizes. Hence, focusing on intervention that target areas where individuals spend "most" time or where transmissibility is higher turn out to be optimal. Therefore, reducing traffic is likely to take a host-vector system into the world of manageable outbreaks.

Merging economics and epidemiology to improve the prediction and management of infectious disease

Mathematical epidemiology, one of the oldest and richest areas in mathematical biology, has significantly enhanced our understanding of how pathogens emerge, evolve, and spread. Classical epidemiological models, the standard for predicting and managing the spread of infectious disease, assume that contacts between susceptible and infectious individuals depend on their relative frequency in the population. The behavioral factors that underpin contact rates are not generally addressed. There is, however, an emerging a class of models that addresses the feedbacks between infectious disease dynamics and the behavioral decisions driving host contact. Referred to as "economic epidemiology" or "epidemiological economics," the approach explores the determinants of decisions about the number and type of contacts made by individuals, using insights and methods from economics. We show how the approach has the potential both to improve predictions of the course of infectious disease, and to support development of novel approaches to infectious disease management.

Disease risk mitigation: the equivalence of two selective mixing strategies on aggregate contact patterns and resulting epidemic spread

The personal choices affecting the transmission of infectious diseases include the number of contacts an individual makes, and the risk-characteristics of those contacts. We consider whether these different choices have distinct implications for the course of an epidemic. We also consider whether choosing contact mitigation (how much to mix) and affinity mitigation (with whom to mix) strategies together has different epidemiological effects than choosing each separately. We use a set of differential equation compartmental models of the spread of disease, coupled with a model of selective mixing. We assess the consequences of varying contact or affinity mitigation as a response to disease risk. We do this by comparing disease incidence and dynamics under varying contact volume, contact type, and both combined across several different disease models. Specifically, we construct a change of variables that allows one to transition from contact mitigation to affinity mitigation, and vice versa. In the absence of asymptomatic infection we find no difference in the epidemiological impacts of the two forms of disease risk mitigation. Furthermore, since models that include both mitigation strategies are underdetermined, varying both results in no outcome that could not be reached by choosing either separately. Which strategy is actually chosen then depends not on their epidemiological consequences, but on the relative cost of reducing contact volume versus altering contact type. Although there is no fundamental epidemiological difference between the two forms of mitigation, the social cost of alternative strategies can be very different. From a social perspective, therefore, whether one strategy should be promoted over another depends on economic not epidemiological factors.

Assessment of the Intensive Countermeasures in the 2009 Pandemic Influenza in Korea

Objectives

Methods

Results

Conclusion

From the guest editors

Carlos Castilo-Chavez is a Regents Professor, a Joaquin Bustoz Jr. Professor of Mathematical Biology, and a Distinguished Sustainability Scientist at Arizona State University. His research program is at the interface of the mathematical and natural and social sciences with emphasis on (i) the role of dynamic social landscapes on disease dispersal; (ii) the role of environmental and social structures on the dynamics of addiction and disease evolution, and (iii) Dynamics of complex systems at the interphase of ecology, epidemiology and the social sciences. Castillo-Chavez has co-authored over two hundred publications (see goggle scholar citations) that include journal articles and edited research volumes. Specifically, he co-authored a textbook in Mathematical Biology in 2001 (second edition in 2012); a volume (with Harvey Thomas Banks) on the use of mathematical models in homeland security published in SIAM's Frontiers in Applied Mathematics Series (2003); and co-edited volumes in the Series Contemporary Mathematics entitled '' Mathematical Studies on Human Disease Dynamics: Emerging Paradigms and Challenges'' (American Mathematical Society, 2006) and Mathematical and Statistical Estimation Approaches in Epidemiology (Springer-Verlag, 2009) highlighting his interests in the applications of mathematics in emerging and re-emerging diseases. Castillo-Chavez is a member of the Santa Fe Institute's external faculty, adjunct professor at Cornell University, and contributor, as a member of the Steering Committee of the '' Committee for the Review of the Evaluation Data on the Effectiveness of NSF-Supported and Commercially Generated Mathematics Curriculum Materials,'' to a 2004 NRC report. The CBMS workshop '' Mathematical Epidemiology with Applications'' lectures delivered by C. Castillo-Chavez and F. Brauer in 2011 have been published by SIAM in 2013.

The effect of public health interventions on the spread of influenza among cities

Infectious disease is no longer a local problem. Modern populations are more mobile than ever before, and with this mobility comes active global mixing of infectious disease. To understand the spread of diseases such as influenza, we use a multi-city epidemic model. We extend the SEIR (susceptible-exposed-infectious-recovered) model to incorporate population migration between cities, and use this model to analyze the geographic spread of influenza. We investigate the effectiveness of travel restrictions as a control against the spread of influenza. First we obtain the basic reproduction number for the single city case, and observe two other control strategies suggested by this case: increasing the number of clinically ill individuals that are treated, and reducing the interval between infection and treatment of such individuals. We evaluate the effectiveness of the three control strategies with numerical simulations. It is shown that travel restrictions are less effective than the other two strategies. In general, travel restriction tends to delay the spread of the disease into new cities. However, it can increase the peak height of infected populations in all cities. An understanding of the epidemiological structures of related cities is strongly recommended in order to effectively apply the travel restriction strategy.

Mitigating effects of vaccination on influenza outbreaks given constraints in stockpile size and daily administration capacity

Background

Influenza viruses are a major cause of morbidity and mortality worldwide. Vaccination remains a powerful tool for preventing or mitigating influenza outbreaks. Yet, vaccine supplies and daily administration capacities are limited, even in developed countries. Understanding how such constraints can alter the mitigating effects of vaccination is a crucial part of influenza preparedness plans. Mathematical models provide tools for government and medical officials to assess the impact of different vaccination strategies and plan accordingly. However, many existing models of vaccination employ several questionable assumptions, including a rate of vaccination proportional to the population at each point in time.

Methods

We present a SIR-like model that explicitly takes into account vaccine supply and the number of vaccines administered per day and places data-informed limits on these parameters. We refer to this as the non-proportional model of vaccination and compare it to the proportional scheme typically found in the literature.

Results

The proportional and non-proportional models behave similarly for a few different vaccination scenarios. However, there are parameter regimes involving the vaccination campaign duration and daily supply limit for which the non-proportional model predicts smaller epidemics that peak later, but may last longer, than those of the proportional model. We also use the non-proportional model to predict the mitigating effects of variably timed vaccination campaigns for different levels of vaccination coverage, using specific constraints on daily administration capacity.

Conclusions

The non-proportional model of vaccination is a theoretical improvement that provides more accurate predictions of the mitigating effects of vaccination on influenza outbreaks than the proportional model. In addition, parameters such as vaccine supply and daily administration limit can be easily adjusted to simulate conditions in developed and developing nations with a wide variety of financial and medical resources. Finally, the model can be used by government and medical officials to create customized pandemic preparedness plans based on the supply and administration constraints of specific communities.