Reproduction Ratio Research Articles

Disparities in PM2.5 exposure and population density influence SARS-CoV-2 transmission among racial and ethnic minorities

There is overwhelming evidence that minority populations across the United States (US) have been disproportionately affected by the coronavirus (COVID-19) pandemic. While the disparities are clear, the reasons underlying the rapidity of disease spread—characterized by the basic reproduction ratio R 0—in these communities are less so. Here, we investigate the relative impact of disparities in socioeconomic, environmental, and lung health factors on R 0 across 12 major metropolitan statistical areas (MSAs) in the US at the county-level. We inferred county-level R 0 values using an established metapopulation epidemiological model corresponding to the period 1 March–30 April 2020 marked by a rapid surge in COVID-19 cases across the US and implementation of strict lockdown measures. We find that disparities in long-term, ambient PM2.5 exposure (particulate matter with aerodynamic diameter 2.5 μm or less) and population density have an outsized effect on R 0 in comparison to disparities in the other factors. We also find that the effect of increasing population density on R 0 is dependent on ambient PM2.5 mass concentrations. PM2.5 exposure has a well-established causal effect with increased risks and severe outcomes during previous infectious disease outbreaks. Overall, we find the largest disparities in PM2.5 exposure, population density, and associated increase in R 0 among the Hispanic-American population, followed by Asian-American and African-American populations, respectively. Longstanding geographic segregation of minority communities has led to extremely large fluctuations in county-level racial and ethnic minority fractions. As such, we conclude by providing each MSA with a roadmap for identifying which communities should be targeted for PM2.5 mitigation policy implementation. The methodology and results of this study may be extrapolatable to the rest of the world toward discerning the consequences of population density and PM2.5 disparities in COVID-19 spread.

Large-scale comparison of biomass and reproductive phenology among native and non-native populations of the seagrass Zostera japonica

Large-scale analysis along latitude or temperature gradients can be an effective method for exploring the potential roles of light and temperature in controlling seagrass phenology. In this study, we investigated effects of latitude and temperature on seagrass biomass and reproductive seasonality.Zostera japonicais an intertidal seagrass with a wide latitudinal distribution expanding from tropical to temperate zones in its native range in Asia, with an additional non-native distribution in North America. We collated available data on phenological traits (timings of peak biomass or reproduction, durations of biomass growth and reproductive season, and maximum biomass or reproductive ratio) from publications and our own observations. Traits were compared among geographic groups: Asia-tropical, Asia-temperate, and North America-temperate. We further examined relationships between traits and latitude and temperature for 3 population groups: Asian, North American, and all populations. Our analysis revealed significant variation among geographic groups in maximum biomass, peak reproductive timing, and maximum reproductive ratio, but not in other traits. Maximum biomass and peak reproductive timing for Asian and all populations were significantly correlated with latitude and temperature. Maximum biomass was highest at mid-latitudes or intermediate temperatures and decreased toward distribution range limits, and peak reproductive timing occurred later in the year at higher latitudes or cooler sites. North American populations showed shorter growth durations and greater reproductive ratios at higher latitude. Different responses observed for North American populations may reflect effects of introduction. Our study demonstrates potential variation among geographic regions and between native and non-native populations.

Production System, Herd Size and Productivity of Local Goats in Liquiçá Municipality, Timor-Leste

Abstract The goat is a species of small ruminants that has great potential in the development of its production. The aim of this study is to obtain basic data on the rearing system used by the breeders, herd size and productivity of goats kept only in a traditional subsistence production system. Data collection period starts on September 20 to December 20, 2018 or is carried out for three months. This study used the survey method and in the process of determining the research site used the intentional sampling. In determining the sample size, Slovin was used, and to determine the respondents, it used the simple random sampling method. The observed variables are herd size, herd structure, gestation period, litter size, weaning age, first giving birth, reproduction ratio and production system. The results of the research showed that all farmers who had been as the respondents generally still used the traditional extensive system, did not pay attention to the problem of feed quality and frequency of feeding; only using local feed as animal feed. alternative supplementary, does not handle the problem of reproduction ratio well, the gestational age of the goats is still normal, the litter size is low, mostly only 1-2 heads per birth, the mortality rate is low at 1%, the age at first calving is 12 months and the weaning age young goats are generally 3months - 6 months. It is recommended to improve the maintenance system with good management to increase the productivity of local goats.

Re-emphasizing mechanism in the community ecology of disease.

1. Hosts and their parasites exist within complex ecological communities. However, the role that non-focal community members, species which cannot be infected by a focal pathogen, may play in altering parasite transmission is often only studied in the lens of the "diversity-disease" relationship by focusing on species richness. This approach largely ignores mechanistic species interactions and risks collapsing our understanding of the community ecology of disease down to defining the prominence of "amplification" vs. "dilution" effects. 2. However, non-focal species vary in their traits, densities, and types of interactions with focal hosts and parasites. Therefore, a community ecology approach based on the mechanisms underlying parasite transmission, host harm, and dynamic species interactions may better advance our understanding of parasite transmission in complex communities. 3. Using the concept of the parasite's basic reproductive ratio, R0, as a generalizable framework, we examine several critical mechanisms by which interactions among hosts, parasites, and non-focal species modulate transmission and provide examples from relevant literature. 4. By focusing on the mechanism by which non-focal species impact transmission, we can emphasize the similarities among classic paradigms in the community ecology of disease, gain new insights into parasite invasion and persistence, community traits correlated with disease dilution or amplification, and the feasibility of biocontrol for parasites of conservation, agricultural, or human health concern.

A rumor spreading pairwise model on weighted networks

The heterogeneity in edge weights plays a crucial role in the spread of a rumor on networks. To investigate the effects of weight distribution on a rumor spreading, we employ an ISR pairwise model on weighted regular random networks in this paper. The basic reproductive ratio of this model is derived, and it indicates that the heterogeneity in transmission rate leads to a fall in the basic reproductive ratio when the average transmission rate is fixed. Besides, by assuming that the transmission rate is a linear function of the edge weight, numerical simulation results of the model on networks with a bimodal weight distribution show that weight heterogeneity has a complex impact on the dynamics of a rumor. When we fix the proportions of two weights but vary their values to keep a constant average weight, increasing the heterogeneity in weight distribution slows down the initial growth rate, the final size and the maximum influence of a rumor, but increases the duration for rumor spreading. However, if we change both the values and proportions of two weights, only the mean and variance of the weight distribution are not able to provide sufficient information to predict the dynamics of a rumor. Moreover, we get similar simulation results in the case of nonlinear transmission rate.

Prediction of COVID-19 evolution in Binh Duong province using SIR epidemiological model

With the fast growth of the COVID-19 pandemic, in-silico studies based on the susceptible-infected-removed (SIR) epidemiological model are very critical to provide reliable predictions of the COVID-19 evolution that can effectively support governments to issue the right measures to prevent and control the pandemic. In this study, the evolution of the COVID-19 in the Binh Duong province is investigated using the SIR model implemented in R-Studio software, in which the homemade computer codes based on the SIR model are developed using R language to automatically detect the optimal parameters in the model, including the reproduction ratio R0, the infection coefficient β, and the recovery coefficient γ. The SIR predictions indicate that the number of new positive cases per day in the Binh Duong province is only 40 new cases by November 30, 2021, and the total number of new cases per day becomes zero by middle February 2021. Besides, the Binh Duong province only has 1126 infected cases by November 30, 2021, which reduces to 203 cases at the end of December 2021. Through the SIR results, the COVID-19 pandemic in the Binh Duong is predicted to be ended at the end of December 2021.

A dengue fever model with free boundary incorporating the time‐periodicity and spatial‐heterogeneity

Propagation of dengue fever is characterized by periodicity and seasonality and further influenced by geographic heterogeneity. To account for these characteristics, we formulate a dengue model in a spatial‐heterogeneous and time‐periodic environment. Moreover, the free boundary is additionally incorporated into our model to reflect the boundary change of region where dengue virus spreads. Employing the properties of the contagion risk threshold, that is the spatial‐temporal basic reproduction ratio, we derive some sufficient conditions regarding the vanishing and spreading of virus. Importantly, the long‐time asymptotic behavior of solution is studied in depth when spreading happens. Our findings manifest that as time goes on, dengue virus will behave periodically when spreading. Finally, these phenomena are numerically simulated and epidemiologically explained.

A study of the COVID‐19 epidemic in India using the SEIRD model

BackgroundThe coronavirus pandemic (COVID‐19) is causing a havoc globally, exacerbated by the newly discovered SARS‐CoV‐2 virus. Due to its high population density, India is one of the most badly effected countries from the first wave of COVID‐19. Therefore, it is extremely necessary to accurately predict the state‐wise and overall dynamics of COVID‐19 to get the effective and efficient organization of resources across India.MethodsIn this study, the dynamics of COVID‐19 in India and several of its selected states with different demographic structures were analyzed using the SEIRD epidemiological model. The basic reproductive ratio R0 was systemically estimated to predict the dynamics of the temporal progression of COVID‐19 in India and eight of its states, Andhra Pradesh, Chhattisgarh, Delhi, Gujarat, Madhya Pradesh, Maharashtra, Tamil Nadu, and Uttar Pradesh.ResultsFor India, the SEIRD model calculations show that the peak of infection is expected to appear around the middle of October, 2020. Furthermore, we compared the model scenario to a Gauss ian fit of the daily infected cases and obtained similar results. The early imposition of a nation‐wide lockdown has reduced the number of infected cases but delayed the appearance of the infection peak significantly.ConclusionAfter comparing our calculations using India’s data to the real life dynamics observed in Italy and Russia, we can conclude that the SEIRD model can predict the dynamics of COVID‐19 with sufficient accuracy.

Evolutionary dynamics of the Trivers-Willard effect: A nonparametric approach.

The Trivers–Willard hypothesis (TWH) states that parents in good condition tend to bias their offspring sex ratio toward the sex with a higher variation in reproductive value, whereas parents in bad condition favor the opposite sex. Although the TWH has been generalized to predict various Trivers–Willard effects (TWE) depending on the life cycle of a species, existing work does not sufficiently acknowledge that sex‐specific reproductive values depend on the relative abundances of males and females in the population. If parents adjust their offspring sex ratio according to the TWE, offspring reproductive values will also change. This should affect the long‐term evolutionary dynamics and might lead to considerable deviations from the original predictions.In this paper, I model the full evolutionary dynamics of the TWE, using a published two‐sex integral projection model for the Columbian ground squirrel (Urocitellus columbianus). Offspring sex ratio is treated as a nonparametric continuous function of maternal condition. Evolutionary change is treated as the successive invasion of mutant strategies. The simulation is performed with varying starting conditions until an evolutionarily stable strategy (ESS) is reached.The results show that the magnitude of the evolving TWE can be far greater than previously predicted. Furthermore, evolutionary dynamics show considerable nonlinearities before settling at an ESS. The nonlinear effects depend on the starting conditions and indicate that evolutionary change is fastest when starting at an extremely biased sex ratio and that evolutionary change is weaker for parents of high condition. The results show neither a tendency to maximize average population fitness nor to minimize the deviation between offspring sex ratio and offspring reproductive value ratio.The study highlights the importance of dynamic feedback in models of natural selection and provides a new methodological framework for analyzing the evolution of continuous strategies in structured populations.

Backward bifurcation in a two strain model of heroin addiction

Among the various causes of heroin addiction, the use of ‎prescription ‎opioids‎ is one of the main reasons. In this article, we introduce and analyze a two ‎strain‎ epidemic model with super infection for modeling the effect of ‎prescrib‎ed opioids abuse on heroin ‎addiction.‎ ‎Our ‎model ‎contains ‎the ‎effect ‎of ‎relapse ‎of ‎individuals ‎under ‎treatment/rehabilitation‎ ‎to drug abuse in each ‎strain.‎ ‎We ‎extract‎ the basic reproductive ‎ratio, ‎the‎ invasion numbers‎, ‎and study the occurrence of backward bifurcation in strain ‎domi‎nance equilibria, i.e., boundary ‎equilibria. ‎Also, ‎we ‎study ‎both‎ ‎‎local and global stability of DFE and boundary equilibria ‎under suitable conditions‎.‎ ‎Furthermore, we study the ‎existence of the coexistence equilibrium point‎. We prove that when ‎$‎R_0<1‎$‎, the coexistence equilibrium point can exist, i.e., backward bifurcation ‎occurs‎ in coexistence equilibria. ‎Finally, we use numerical simulation to describe the obtained analytical results.‎

The role of ingroup assortative sociality in the COVID-19 pandemic: A multilevel analysis of google trends data in the United States

This study tested how family ties and religiosity, two extended elements of ingroup assortative sociality, would predict group-level COVID-19 severity in the U.S. and how COVID-19 threat would predict ingroup assortative sociality at a weekly level. Multilevel models which analyzed the state-level archival (e.g., religious participation) and Google trends data (e.g., marriage for family ties; prayer for religiosity) on ingroup assortative sociality showed that religious search volume (from 2004 to 2019) significantly and negatively predicted COVID-19 severity (i.e., shorter time delay of first documented cases, shorter overall doubling times, higher reproductive ratio and higher case fatality ratio) across states (Study 1a) and counties (Study 1b) while search volume for family ties only significantly and negatively predicted county-level COVID-19 severity. Multilevel analyses also found that weekly COVID-19 severity weakly predicted weekly search volume of marriage and religion (Study 2a), but when COVID-19 threat was in the collective consciousness in a given week (i.e., Google search volume for coronavirus within 52 weeks), collective levels of ingroup assortative sociality increased from the previous week (Study 2b). Evidence across studies suggested that religiosity, compared with family ties, could serve a more important role for the U.S. people during the deadly pandemic.

Qualitative analysis of a mathematical model with presymptomatic individuals and two SARS-CoV-2 variants

The SARS-CoV-2 continues to spread across the world. During this COVID-19 pandemic, several variants of the SARS-CoV-2 have been found. Some of these new variants like the VOC-202012/01 of lineage B.1.1.7 or the most recently B.1.617 emerging in India have a higher infectiousness than those previously prevalent. We propose a mathematical model based on ordinary differential equations to investigate potential consequences of the appearance of a new more transmissible SARS-CoV-2 strain in a given region. The proposed mathematical model incorporates the presymptomatic and asymptomatic subpopulations in addition to the more usual susceptible, exposed, infected, and recovered subpopulations. This is important from a realistic point of view since it has been found recently that presymptomatic and asymptomatic individuals are relevant spreaders of the SARS-CoV-2. Using the next-generation matrix method, we find the basic reproduction number, {mathcal {R}}_{0}, an important threshold parameter that provides insight regarding the evolution and outcome of a certain instance of the COVID-19 pandemic. The local and global stability of system equilibria are also presented. In particular, for the global stability we construct a Lyapunov functional and use the LaSalle invariant principle to prove that if the basic reproduction ratio is less than unity, the infection-free equilibrium is globally asymptotically stable. On the other hand, if {mathcal {R}}_{0}>1 the endemic equilibrium is globally asymptotically stable. Finally, we present numerical simulations to numerically support the analytic results and to show the impact of the introduction of a new more contagious SARS-CoV-2 variant in a population.

Threshold Dynamics of a Non-Linear Stochastic Viral Model with Time Delay and CTL Responsiveness.

This article focuses on a stochastic viral model with distributed delay and CTL responsiveness. It is shown that the viral disease will be extinct if the stochastic reproductive ratio is less than one. However, when the stochastic reproductive ratio is more than one, the viral infection system consists of an ergodic stationary distribution. Furthermore, we obtain the existence and uniqueness of the global positive solution by constructing a suitable Lyapunov function. Finally, we illustrate our results by numerical simulation.

Mammal assemblage composition predicts global patterns in emerging infectious disease risk

As a source of emerging infectious diseases, wildlife assemblages (and related spatial patterns) must be quantitatively assessed to help identify high‐risk locations. Previous assessments have largely focussed on the distributions of individual species; however, transmission dynamics are expected to depend on assemblage composition. Moreover, disease–diversity relationships have mainly been studied in the context of species loss, but assemblage composition and disease risk (e.g. infection prevalence in wildlife assemblages) can change without extinction. Based on the predicted distributions and abundances of 4466 mammal species, we estimated global patterns of disease risk through the calculation of the community‐level basic reproductive ratio R0, an index of invasion potential, persistence, and maximum prevalence of a pathogen in a wildlife assemblage. For density‐dependent diseases, we found that, in addition to tropical areas which are commonly viewed as infectious disease hotspots, northern temperate latitudes included high‐risk areas. We also forecasted the effects of climate change and habitat loss from 2015 to 2035. Over this period, many local assemblages showed no net loss of species richness, but the assemblage composition (i.e. the mix of species and their abundances) changed considerably. Simultaneously, most areas experienced a decreased risk of density‐dependent diseases but an increased risk of frequency‐dependent diseases. We further explored the factors driving these changes in disease risk. Our results suggest that biodiversity and changes therein jointly influence disease risk. Understanding these changes and their drivers and ultimately identifying emerging infectious disease hotspots can help health officials prioritize resource distribution.

The introduction of a mandatory mask policy was associated with significantly reduced COVID-19 cases in a major metropolitan city.

BackgroundWhilst evidence of use of face masks in reducing COVID-19 cases is increasing, the impact of mandatory use across a large population has been difficult to assess. Introduction of mandatory mask use on July 22, 2020 during a resurgence of COVID-19 in Melbourne, Australia created a situation that facilitated an assessment of the impact of the policy on the epidemic growth rate as its introduction occurred in the absence of other changes to restrictions.Methods and findingsExponential epidemic growth or decay rates in daily COVID-19 diagnoses were estimated using a non-weighted linear regression of the natural logarithm of the daily cases against time, using a linear spline model with one knot (lspline package in R v 3.6.3). The model’s two linear segments pivot around the hinge day, on which the mask policy began to take effect, 8 days following the introduction of the policy. We used two forms of data to assess change in mask usage: images of people wearing masks in public places obtained from a major media outlet and population-based survey data. Potential confounding factors (including daily COVID-19 tests, number of COVID-19 cases among population subsets affected differentially by the mask policy–e.g., healthcare workers) were examined for their impact on the results. Daily cases fitted an exponential growth in the first log-linear segment (k = +0.042, s.e. = 0.007), and fitted an exponential decay in the second (k = -0.023, s.e. = 0.017) log-linear segment. Over a range of reported serial intervals for SARS-CoV-2 infection, these growth rates correspond to a 22–33% reduction in an effective reproduction ratio before and after mandatory mask use. Analysis of images of people in public spaces showed mask usage rose from approximately 43% to 97%. Analysis of survey data found that on the third day before policy introduction, 44% of participants reported “often” or “always” wearing a mask; on the fourth day after, 100% reported “always” doing so. No potentially confounding factors were associated with the observed change in growth rates.ConclusionsThe mandatory mask use policy substantially increased public use of masks and was associated with a significant decline in new COVID-19 cases after introduction of the policy. This study strongly supports the use of masks for controlling epidemics in the broader community.

ENGINEERING MATHEMATICAL MODELLING OF CORONA VIRUS (COVID-19) TRANSMISSION IN IRAQ

Purpose: As a result of a sudden spreading of an epidemic novel virus, scientifically named COVID-19, this paper has been done to present a contribution towards fighting this virus in Iraq. &#x0D; Methodology: This investigation is focusing on constructing an engineering mathematical model based on the Suspected, Infected, and Recovered model (SIR), given by Kermack and McKendrick. &#x0D; Main Findings: Iraqi people are facing and suffering from this COVID-19. Three governorates occupying the locally highest infection levels, plus the world's highest deaths to infected cases ratio of about 11%, are Baghdad, Sulaimani, and Karbala. &#x0D; Implications: It is showed that the Reproduction ratio R0)K is positive (greater than 1) in the three nominated zones, which means that the epidemic disease will keep spreading in a broad manner and depending on many specific factors. Many effective recommendations are presented to avoid spreading this novel virus via many techniques. &#x0D; Novelty: SIR model is used to assess epidemic levels in 3 zones.

A robust phenomenological approach to investigate COVID-19 data for France

We provide a new method to analyze the COVID-19 cumulative reported cases data based on a two-step process: first we regularize the data by using a phenomenological model which takes into account the endemic or epidemic nature of the time period, then we use a mathematical model which reproduces the epidemic exactly. This allows us to derive new information on the epidemic parameters and to compute the effective basic reproductive ratio on a daily basis. Our method has the advantage of identifying robust trends in the number of new infectious cases and produces an extremely smooth reconstruction of the epidemic. The number of parameters required by the method is parsimonious: for the French epidemic between February 2020 and January 2021 we use only 11 parameters in total.

Group-level human values estimated with web search data and archival data explain the geographic variation in COVID-19 severity in the United States

Objectives Personal Focus values (PF = Openness to change values + Self-enhancement values) motivate self-centred behaviours, and Social Focus values (SF = Conservation values + Self-transcendence values) promote self-sacrificial counterparts. This research investigated how a state-level PF-SF value-continuum would explain the geographic variation in COVID-19 severity in the United States. Design This research estimated state-level values by Google search data (from 2004 to 2019) on value-related words (e.g. family for conservation values) (Study 1a) and archival indicators (e.g. gun ownership rate for security values) (Study 1b). COVID-19 severity was measured by shorter time delay of first documented cases, shorter overall doubling times, higher reproductive ratio and higher case fatality ratio. Hierarchical and multilevel analyses examined how state-level values would predict COVID-19 severity across U.S. states (Studies 1a and 1b) and 3,135 counties (Study 2). Results State-level analyses accounting for spatial autocorrelation and covariates (e.g. COVID-19 testing rate, airport traffic, personality, etc.) revealed that the PF-SF value-continuums measured with different methods positively and significantly predicted COVID-19 severity, and the effects of state-level values on county-level COVID-19 severity were significant when county- and state-level covariates were controlled. Conclusion Social focus values may mitigate the devastating effect of COVID-19 in the United States.

Critical fluctuations in epidemic models explain COVID-19 post-lockdown dynamics

As the COVID-19 pandemic progressed, research on mathematical modeling became imperative and very influential to understand the epidemiological dynamics of disease spreading. The momentary reproduction ratio r(t) of an epidemic is used as a public health guiding tool to evaluate the course of the epidemic, with the evolution of r(t) being the reasoning behind tightening and relaxing control measures over time. Here we investigate critical fluctuations around the epidemiological threshold, resembling new waves, even when the community disease transmission rate beta is not significantly changing. Without loss of generality, we use simple models that can be treated analytically and results are applied to more complex models describing COVID-19 epidemics. Our analysis shows that, rather than the supercritical regime (infectivity larger than a critical value, beta > beta _c) leading to new exponential growth of infection, the subcritical regime (infectivity smaller than a critical value, beta < beta _c) with small import is able to explain the dynamic behaviour of COVID-19 spreading after a lockdown lifting, with r(t) approx 1 hovering around its threshold value.

Vaccine escape in a heterogeneous population: insights for SARS-CoV-2 from a simple model.

As a countermeasure to the SARS-CoV-2 pandemic, there has been swift development and clinical trial assessment of candidate vaccines, with subsequent deployment as part of mass vaccination campaigns. However, the SARS-CoV-2 virus has demonstrated the ability to mutate and develop variants, which can modify epidemiological properties and potentially also the effectiveness of vaccines. The widespread deployment of highly effective vaccines may rapidly exert selection pressure on the SARS-CoV-2 virus directed towards mutations that escape the vaccine-induced immune response. This is particularly concerning while infection is widespread. By developing and analysing a mathematical model of two population groupings with differing vulnerability and contact rates, we explore the impact of the deployment of vaccines among the population on the reproduction ratio, cases, disease abundance and vaccine escape pressure. The results from this model illustrate two insights: (i) vaccination aimed at reducing prevalence could be more effective at reducing disease than directly vaccinating the vulnerable; (ii) the highest risk for vaccine escape can occur at intermediate levels of vaccination. This work demonstrates a key principle: the careful targeting of vaccines towards particular population groups could reduce disease as much as possible while limiting the risk of vaccine escape.