Probability Of Outbreak Research Articles

An Analysis of CTMC Stochastic Models with Quarantine on the Spread of Tuberculosis Diseases

The SIQRS epidemic model developed in this study is intended to analyze the spread characteristics of the infectious disease tuberculosis. It is a modification of the SIQR model developed by Cao et al., using a stochastic model called the Continuous Time Markov Chains (CTMC) approach. Further analysis of the SIQRS model was done to determine the transitional probability, the outbreak probability, the expected time until disease extinction and to simulate the effect of quarantine treatment on the expected time until disease extinction. Based on the simulation it can be concluded that a decrease of the healing rate together with an increase of the transmission rate changes the basic reproduction number (R0), the expected number of infected individuals (m), the time until disease extinction, and the outbreak probability. A disease outbreak will occur if both R0>1 and m>1 hold. Also, based on the simulation it was concluded that the decrease of the healing rate and the increase of the transmission rate cause increases of R0 and m. An increase of the quarantine rate reduces the expected time to disease extinction, R0 and m. As a consequence, the disease will gradually disappear from the system.

Mathematical Modeling and COVID-19 Forecast in Texas, USA: A Prediction Model Analysis and the Probability of Disease Outbreak.

Response to the unprecedented coronavirus disease 2019 (COVID-19) outbreak needs to be augmented in Texas, United States, where the first 5 cases were reported on March 6, 2020, and were rapidly followed by an exponential rise within the next few weeks. This study aimed to determine the ongoing trend and upcoming infection status of COVID-19 in county levels of Texas. Data were extracted from the following sources: published literature, surveillance, unpublished reports, and websites of Texas Department of State Health Services (DSHS), Natality report of Texas, and WHO Coronavirus Disease (COVID-19) Dashboard. The 4-compartment Susceptible-Exposed-Infectious-Removal (SEIR) mathematical model was used to estimate the current trend and future prediction of basic reproduction number and infection cases in Texas. Because the basic reproduction number is not sufficient to predict the outbreak, we applied the Continuous-Time Markov Chain (CTMC) model to calculate the probability of the COVID-19 outbreak. The estimated mean basic reproduction number of COVID-19 in Texas is predicted to be 2.65 by January 31, 2021. Our model indicated that the third wave might occur at the beginning of May 2021, which will peak at the end of June 2021. This prediction may come true if the current spreading situation/level persists, i.e., no clinically effective vaccine is available, or this vaccination program fails for some reason in this area. Our analysis indicates an alarming ongoing and upcoming infection rate of COVID-19 at county levels in Texas, thereby emphasizing the promotion of more coordinated and disciplined actions by policy-makers and the population to contain its devastating impact.

HAZARD ASSESSMENT OF DEBRIS FLOW ALONG ZIRE-BOMI SECTION OF SICHUAN-TIBET RAILWAY

Debris flow is one of the most developed disaster types in the Tibetan Plateau, and poses a certain threat to the construction of Sichuan-Tibet Railway and the safety of life and property due to its sudden outbreak, violent movement process, and strong destructive characteristics. Geological hazard assessment is part of the effective measures in the management and prevention of disaster prevention and mitigation. For reasonably quantifying the spatial distribution characteristics of debris flow hazard along the railway line, the paper takes the Zire-Bomi section of the Sichuan-Tibet Railway within Linzhi City as the experimental area. We apply the Bayesian optimization algorithm based random forest(TBOR) and gradient boosting tree model(TBOG)to quantify the debris flow hazard values of the route section and identify the hazard levels. The input information of the two models includes 172 historical hazard points and 11 feature parameters. The output information is the outbreak probability of debris flow for each prediction unit. Lastly, the ROC-AUC method is employed to test the evaluation outcomes of the two prediction algorithms. The findings reveal that in the TBOR and TBOG models, the overall debris flow hazard level of the Zire-Bomi line section of the Sichuan-Tibet Railway is relatively high. The proportion of hazard zones inside the high-higher hazard interval of the two models reaches 56.439% and 66.580% respectively, corresponding to the highest hazard point densities of 12.577/(102km2) and 12.940/(102km2)respectively. Compared to the ROC-AUC values derived from the TBOG model, the TBOR model computes 0.89, which is higher than that of TBOR at 0.83. Therefore, the TBOR model has better prediction accuracy. The research findings can provide necessary reference for the construction of disaster prevention and mitigation protection projects along Sichuan-Tibet Railway and the hazard evaluation of other line sections.

Significant decline of malaria incidence in a low socioeconomic area in the southeast of Iran: 10years field assessment during malaria elimination programme.

Although malaria burden and its active foci have sharply declined after the implementation of elimination programme since 2010, it is still considered as a major public health problem in southeast Iran. This descriptive-analytical study aimed to determine 10-years of malaria epidemiological trends in Bashagard County. Data were collected from 7 selected malarious region of the county based on active and passive surveillance of clinical cases. For diagnosis of malaria, the examination of microscopic slides and rapid diagnostic test, were used. In total, 237 malaria cases were found from 2009 to 2018. Plasmodium vivax was the dominant parasite species and identified in 232 (97.9%) individuals. Males were infected more than females and the majority of malaria cases (67.4%) were recorded from rural areas. Although about 98% of malaria cases were indigenous, they have decreased form 200 cases in 2009 to zero indigenous transmission in 2018. During the study period, malaria cases had decreased significantly by about 99% and the incidence rate had declined from 5.47/1000 cases in 2009 to 0.002/1000 in 2018. The incidence of malaria, especially indigenous cases, in Bashagard County has decreased dramatically in the past 10years. However, there is still probability of malaria re-introduction and outbreak in the county due to climatical and geographical conditions. Therefore, it is necessary to implement an active surveillance system to detect and treat malaria cases quickly, during the elimination phase in this county.

Stochastic models of infectious diseases in a periodic environment with application to cholera epidemics.

Seasonal variation affects the dynamics of many infectious diseases including influenza, cholera and malaria. The time when infectious individuals are first introduced into a population is crucial in predicting whether a major disease outbreak occurs. In this investigation, we apply a time-nonhomogeneous stochastic process for a cholera epidemic with seasonal periodicity and a multitype branching process approximation to obtain an analytical estimate for the probability of an outbreak. In particular, an analytic estimate of the probability of disease extinction is shown to satisfy a system of ordinary differential equations which follows from the backward Kolmogorov differential equation. An explicit expression for the mean (resp.variance) of the first extinction time given an extinction occurs is derived based on the analytic estimate for the extinction probability. Our results indicate that the probability of a disease outbreak, and mean and standard derivation of the first time to disease extinction are periodic in time and depend on the time when the infectious individuals or free-living pathogens are introduced. Numerical simulations are then carried out to validate the analytical predictions using two examples of the general cholera model. At the end, the developed theoretical results are extended to more general models of infectious diseases.

Epidemiological analysis of asymptomatic SARS-CoV-2 transmission in the community: an individual-based model

We established an individual-based computer model to simulate the occurrence, infection, discovery, quarantine, and quarantine release (recovery) of asymptomatic SARS-CoV-2 infected individuals or patients within the community. The model was used to explore the effects of control measures, such as active tracing, laboratory testing, active treatment, and home quarantine on the epidemic. Considering the condition that R0 = 1.2, when a case of an imported asymptomatic infected individual (AII) was reported in the community, the implementation of control measures reduced the number of AIIs and patients by 62.2% and 62.4%, respectively. The number of undetected AIIs and patients peaked at 302 days of the epidemic, reaching 53 and 20 individuals, respectively. The implementation of sustained active tracing, laboratory testing, active treatment, and home quarantine can significantly reduce the probability of disease outbreaks and block the spread of the COVID-19 epidemic caused by AIIs in the community.

Factors determining household-level food insecurity during COVID-19 epidemic: a case of Wuhan, China.

BackgroundIn coping with the coronavirus disease 2019 (COVID-19) epidemic, cities adopted social isolation and lockdown measures; however, little is known about the impacts of these restrictions on household food security.ObjectiveThis study provides a timely assessment of household food insecurity (HFI) in the Chinese city of Wuhan during the COVID-19 epidemic period and also investigates its determinant factors.DesignWe collected valid data on food insecurity from 653 households in Wuhan via an online questionnaire in March 2020. The Household Food Insecurity Access Scale Score (HFIASS) was used to measure HFI, and a multiple linear regression model was used to determine the HFIASS.ResultsThe mean HFIASS in Wuhan was 9.42 (standard deviation: 5.82), with more than 50% of the households had an HFIASS < 9. Compared with normal conditions, lockdown measures had a huge negative impact on household food security. The results revealed that socio-demographic characteristics remained the underlying determinants of HFIASS during the epidemic. Households in Wuhan with local Hukou (city household registration) and self-owned property had a lower risk of food insecurity.Discussion and conclusionAfter the restriction of conventional food access channels, intermediary food purchase methods such as group purchasing, shopping with the help of neighborhood committees, property management agents, and volunteers became the most important or the only channel for residents to access food. There were similarities in the use of these intermediary channels. Based on the probability that the epidemic will continue and the probability of similar public health-related outbreaks in the future, the study calls for a more resilient and responsive sustainable food supply system by harnessing the capacity of communities, e-commerce and rapid logistics.

On the effect of postponing pregnancy in a Zika transmission model

We construct a Zika transmission model to investigate the effect of postponing pregnancy on the infection intensity. We perform analytical and numerical investigations for deterministic and stochastic analysis to obtain the basic reproductive ratio, endemic state, probability of disease extinction, and the probability of outbreak. The results indicate that by reducing the pregnancy rate the mosquito-to-human ratio increases, and, consequently, the basic reproductive ratio increases. Simultaneously, the probability of disease extinction decreases, and the probability of disease outbreak increases. On the other hand, the endemic state of infected infants initially increases with the decrease of the pregnancy recruitment rate, up to a certain level, and decreases as the recruitment rate of pregnancy tends to zero. This work highlights that postponing pregnancy that gives the individual temporary protection for unexpected infected newborns may increase the population infectivity.

Swine producer willingness to pay for Tier 1 disease risk mitigation under multifaceted ambiguity

AbstractUnderstanding producers' risk perceptions of foreign animal diseases and their willingness to participate in prevention efforts is crucial for effective policymaking and communication strategies, yet there is limited empirical evidence to support initiatives. Using results from a 2017 survey of U.S. swine producers, we test whether producers' subjective risk perceptions affect their willingness to pay for interventions that could reduce potential foreign animal disease losses. Then, controlling for subjective risk perceptions, we determine the relative and total impacts of different representations of ambiguity on willingness to pay. Our results indicate that producers have a mean willingness to pay of $0.581 per pig per year. We find no difference in willingness to pay under alternative representations of ambiguity characterized by probability of occurrence, damage, and damage duration. Rather, a producer with a higher subjective belief in the probability of a foreign animal disease outbreak in the U.S. swine industry, a higher level of formal education, operating as a contractor or integrator, and owning a truck‐wash has a higher willingness to pay. Also, the number of market hogs sold annually is an important determinant of willingness to pay for foreign animal disease risk mitigation. [EconLit citations: D81, Q11, Q18]

The importance of supplementary immunisation activities to prevent measles outbreaks during the COVID-19 pandemic in Kenya

BackgroundThe COVID-19 pandemic has disrupted routine measles immunisation and supplementary immunisation activities (SIAs) in most countries including Kenya. We assessed the risk of measles outbreaks during the pandemic in Kenya as a case study for the African Region.MethodsCombining measles serological data, local contact patterns, and vaccination coverage into a cohort model, we predicted the age-adjusted population immunity in Kenya and estimated the probability of outbreaks when contact-reducing COVID-19 interventions are lifted. We considered various scenarios for reduced measles vaccination coverage from April 2020.ResultsIn February 2020, when a scheduled SIA was postponed, population immunity was close to the herd immunity threshold and the probability of a large outbreak was 34% (8–54). As the COVID-19 contact restrictions are nearly fully eased, from December 2020, the probability of a large measles outbreak will increase to 38% (19–54), 46% (30–59), and 54% (43–64) assuming a 15%, 50%, and 100% reduction in measles vaccination coverage. By December 2021, this risk increases further to 43% (25–56), 54% (43–63), and 67% (59–72) for the same coverage scenarios respectively. However, the increased risk of a measles outbreak following the lifting of all restrictions can be overcome by conducting a SIA with ≥ 95% coverage in under-fives.ConclusionWhile contact restrictions sufficient for SAR-CoV-2 control temporarily reduce measles transmissibility and the risk of an outbreak from a measles immunity gap, this risk rises rapidly once these restrictions are lifted. Implementing delayed SIAs will be critical for prevention of measles outbreaks given the roll-back of contact restrictions in Kenya.

Epidemic spreading in a weighted pig trade network

The inclusion of edge weights can add valuable insights in the spreading processes within trade networks and may identify factors influencing the final epidemic size. The aim of the study was to evaluate the effect of different network versions on the outcome of an epidemiological model. The weighted network versions included the number of trade contacts (A), the sum of delivered livestock (B) and the mean number of delivered livestock per trade contact (C). Furthermore, other factors, e.g. transmission probability and farm type of primary outbreak, were tested for their impact on the final epidemic size. From 2013–2014, data from a pig trade network in Northern Germany was recorded containing 678 farms connected by 1,018 directed edges. An epidemiological model was implemented considering a higher probability of disease spread for edges with a higher weight for each of the combinations between network version and transmission probability. Only transmission routes following the network structure were considered for disease transmission. The outcome of the epidemiological model (number of infected farms) was tested with a generalized linear mixed model including the fixed effects network version (unweighted, A, B, C), transmission probability and farm type of primary outbreak (breeding farm, farrowing farm, finishing farm, farrow-to-finishing farm, unknown) as well as all twofold interactions. The results revealed that all fixed effects as well as all twofold interactions were significant (p ≤ 0.05), i.e. in the following only the impact of the interactions on the number of infected farms can be interpreted. Network versions B and C showed in all combinations the highest number of infected farms independent of the underlying transmission probability. The unweighted network and network version A showed a significant increase of infected farms with increasing transmission probability. All interactions including the farm type of primary outbreak revealed a significant higher number of infected farms for farm types located at the beginning of the production chain, e.g. breeding farms. These farm types reached also more other farms in 1–4 steps compared to farm types located near to the end of the production chain. The inclusion of edge weights has a significant effect on the outcome of epidemiological models and dependent on the chosen edge weight the results need to be interpreted accordingly.

Effectiveness of Contact Tracing, Mask Wearing and Prompt Testing on Suppressing COVID-19 Resurgences in Megacities: An Individual-Based Modelling Study

Background: Secondary waves of COVID-19 loom in many countries as strict physical distancing measures have been lifted. Since megacities have been hardest hit by the disease, science-based guidelines of non-pharmaceutical interventions are still in need for post-epidemic management in ‘business as usual’ cities before vaccines are widely available. This study aims to investigate the combined effects of contact tracing, mask wearing, and prompt testing on minimizing the risk of next COVID-19 waves in megacities. Methods: We integrated 5·8 million mobile phone users’ trajectory records into a spatially explicit individual-based model for simulating COVID-19 spread among 4·5 million households, 230 thousand workplaces (including schools), and other public places in 0.6 million buildings in Shenzhen city, China, which has been gradually reopened. Government interventions were incorporated to reconstruct the actual course of the 1st wave epidemic. After validated by empirical data, the model was used to assess the probability of resurgences if sporadic cases occurred in a fully reopened city under different scenarios of contact tracing settings (household, work, school, and public place), mask use, and test-seeking behavior along with receding public vigilance. Findings: Our model well predicted the spatiotemporal dynamics of the 1st wave epidemic in Shenzhen, by age distribution of symptomatic cases, and household secondary attack rate (11·02%). After city reopens, our results show a 50% chance or less of suppressing disease resurgence if not implementing contact tracing. Tracing household contacts, in combination with mandatory (100% compliance) mask use and prompt testing could limit the probability of next outbreak under 5%. If contact tracing can be expanded to work/class group members, the public compliance of masking and testing can be relaxed to 80% and 40%, respectively, to achieve the same suppression target. Further scaled-up contact tracing that includes casual contacts can suppress resurgences with a low compliance to mask use (40%) and prompt testing (20%-40%). Interpretation: To minimize the risk of resurgence in a reopened city, the local government is expected to spare no efforts to trace close contacts in household, workplace and school for a confirmed case. The authorities should promote mask use in a public space and encourage people with COVID-19-like symptoms to testing within two days after illness onset, along with measures such as sick leave compensation and extensive temperature screening in public places.Funding Statement: National Scientific Foundation of China, R & D project of key areas in Guangdong Province, Bill & Melinda Gates Foundation, Joint Engineering Research Center for Health Big Data Intelligent Analysis TechnologyDeclaration of Interests: We declare no competing interests.

Disease-driven reduction in human mobility influences human-mosquito contacts and dengue transmission dynamics.

Heterogeneous exposure to mosquitoes determines an individual’s contribution to vector-borne pathogen transmission. Particularly for dengue virus (DENV), there is a major difficulty in quantifying human-vector contacts due to the unknown coupled effect of key heterogeneities. To test the hypothesis that the reduction of human out-of-home mobility due to dengue illness will significantly influence population-level dynamics and the structure of DENV transmission chains, we extended an existing modeling framework to include social structure, disease-driven mobility reductions, and heterogeneous transmissibility from different infectious groups. Compared to a baseline model, naïve to human pre-symptomatic infectiousness and disease-driven mobility changes, a model including both parameters predicted an increase of 37% in the probability of a DENV outbreak occurring; a model including mobility change alone predicted a 15.5% increase compared to the baseline model. At the individual level, models including mobility change led to a reduction of the importance of out-of-home onward transmission (R, the fraction of secondary cases predicted to be generated by an individual) by symptomatic individuals (up to -62%) at the expense of an increase in the relevance of their home (up to +40%). An individual’s positive contribution to R could be predicted by a GAM including a non-linear interaction between an individual’s biting suitability and the number of mosquitoes in their home (>10 mosquitoes and 0.6 individual attractiveness significantly increased R). We conclude that the complex fabric of social relationships and differential behavioral response to dengue illness cause the fraction of symptomatic DENV infections to concentrate transmission in specific locations, whereas asymptomatic carriers (including individuals in their pre-symptomatic period) move the virus throughout the landscape. Our findings point to the difficulty of focusing vector control interventions reactively on the home of symptomatic individuals, as this approach will fail to contain virus propagation by visitors to their house and asymptomatic carriers.

Effectiveness of Contact Tracing, Mask Wearing and Prompt Testing on Suppressing COVID-19 Resurgences in Megacities: An Individual-Based Modelling Study

Background: As COVID-19 resurges in many countries, science-based guidelines of non-pharmaceutical interventions are still in need for post-epidemic management in ‘business as usual’ cities before vaccines are widely available. This study aims to investigate the combined effects of contact tracing, mask wearing, and prompt testing on minimizing the risk of next COVID-19 waves caused by sporadic outbreaks in megacities.Methods: We integrated large-scale mobile phone tracking data into a spatially explicit individual-based model to simulate COVID-19 spread among 11.2 million individuals in Shenzhen City, China. Government interventions were incorporated to reconstruct the actual course of the 1st wave epidemic. After validated by empirical data, the model was used to assess the probability of resurgences if sporadic cases occurred in a fully reopened city under different scenarios of contact tracing settings (household, work, school, and public place), mask use, and test-seeking behavior along with receding public vigilance.Results: Our model well predicted the spatiotemporal dynamics of the 1st wave epidemic in Shenzhen, by age distribution of symptomatic cases, and household secondary attack rate (11·02%). After city reopens, our results show a 50% chance or less of suppressing disease resurgence if not implementing contact tracing. Tracing household contacts, in combination with mandatory (100% compliance) mask use and prompt testing could limit the probability of next outbreak under 5%. If contact tracing can be expanded to work/class group members, the public compliance of masking and testing can be relaxed to 80% and 40%, respectively, to achieve the same suppression target. Further scaled-up contact tracing that includes casual contacts can suppress resurgences with a low compliance to mask use (40%) and prompt testing (20%-40%). Conclusions: To minimize the risk of resurgence in a reopened city, the local government is expected to spare no efforts to trace close contacts in household, workplace and school for a confirmed case. The authorities should promote mask use in a public space and encourage people with COVID-19-like symptoms to testing within two days after illness onset, along with measures such as sick leave compensation and extensive temperature screening in public places.

Management of vampire bats and rabies: a precaution for rewilding projects in the Neotropics

Changes in animal population dynamics and community composition following species (re)introduction may have unanticipated consequences for a variety of downstream ecosystem processes, including infectious disease transmission. Due the lessons learned from ongoing projects, we present a novel approach on how to anticipate, monitor, and mitigate the vampire bats and rabies in rewilding projects. We pinpoint a series of precautions and the need for long-term monitoring of vampire bats and rabies responses to rewilding projects and highlighted the importance of multidisciplinary teams of scientist and managers focusing on prevention educational program of rabies risk transmitted by bats. In addition, monitoring the relative abundance of vampire bats, considering reproductive control by sterilization and oral vaccines that autonomously transfer among bats would reduce the probability, size and duration of rabies outbreaks. The rewilding assessment framework presented here responds to calls to better integrate the science and practice of rewilding and will help conservation practitioners and researchers to develop effective message framing strategies that minimize bats emerging infectious diseases and support biodiversity and its associated ecosystem services.

Deterministic and stochastic models for the epidemic dynamics of COVID-19 in Wuhan, China.

In this paper, deterministic and stochastic models are proposed to study the transmission dynamics of the Coronavirus Disease 2019 (COVID-19) in Wuhan, China. The deterministic model is formulated by a system of ordinary differential equations (ODEs) that is built upon the classical SEIR framework. The stochastic model is formulated by a continuous-time Markov chain (CTMC) that is derived based on the ODE model with constant parameters. The nonlinear CTMC model is approximated by a multitype branching process to obtain an analytical estimate for the probability of a disease outbreak. The local and global dynamics of the disease are analyzed by using the deterministic model with constant parameters, and the result indicates that the basic reproduction number $ \mathcal{R}_0 $ serves as a sharp disease threshold: the disease dies out if $ \mathcal{R}_0\le 1 $ and persists if $ \mathcal{R}_0 > 1 $. In contrast to the deterministic dynamics, the stochastic dynamics indicate that the disease may not persist when $ \mathcal{R}_0 > 1 $. Parameter estimation and validation are performed to fit our ODE model to the public reported data. Our result indicates that both the exposed and infected classes play an important role in shaping the epidemic dynamics of COVID-19 in Wuhan, China. In addition, numerical simulations indicate that a second wave of the ongoing pandemic is likely to occur if the prevention and control strategies are not implemented properly.

Evolution and Adaptation of Forest and Crop Pathogens in the Anthropocene.

Anthropocene marks the era when human activity is making a significant impact on earth, its ecological and biogeographical systems. The domestication and intensification of agricultural and forest production systems have had a large impact on plant and tree health. Some pathogens benefitted from these human activities and have evolved and adapted in response to the expansion of crop and forest systems, resulting in global outbreaks. Global pathogen genomics data including population genomics and high-quality reference assemblies are crucial for understanding the evolution and adaptation of pathogens. Crops and forest trees have remarkably different characteristics, such as reproductive time and the level of domestication. They also have different production systems for disease management with more intensive management in crops than forest trees. By comparing and contrasting results from pathogen population genomic studies done on widely different agricultural and forest production systems, we can improve our understanding of pathogen evolution and adaptation to different selection pressures. We find that in spite of these differences, similar processes such as hybridization, host jumps, selection, specialization, and clonal expansion are shaping the pathogen populations in both crops and forest trees. We propose some solutions to reduce these impacts and lower the probability of global pathogen outbreaks so that we can envision better management strategies to sustain global food production as well as ecosystem services.

Forecasting the probability of local dengue outbreaks in Queensland, Australia

The global incidence of dengue is increasing, and many previously unaffected areas have reported local cases of the vector-borne disease in recent years. For the effective containment of local outbreaks health authorities rely on the prompt notification of new cases. However, due to severe under-reporting and misdiagnosis, non-endemic countries face difficulties in containing local outbreaks, and the possibility of dengue becoming endemic. Outbreak control measures in non-endemic countries are largely reactive and health authorities would benefit from a universal early warning system that forecasts the probability of dengue outbreaks for given times and locations. We develop a model that establishes a link between pre- and post-border risk of dengue outbreaks. Specifically, we predict the probability of travellers importing dengue from other countries as well as the probability of those travellers causing local outbreaks. Our model can act as an early warning system, forecasting likely times and places of dengue outbreaks. We run our model for the Australian state of Queensland over a period of twelve years. Our results reveal the airports where dengue infected travellers are most likely to arrive and geographic locations associated with high outbreak probabilities. Our results can be used by health authorities to better utilise prevention and control resources and lead to the development of new prevention measures.

Climate‐linked freshwater habitat change will have cost implications: Pest blackfly outbreaks in two linked South African rivers

AbstractCalls for implementation of environmental flows have been growing over the past 20 years, and their implementation is now being recognized. The need for such assessments to occur in conjunction with maintenance of environmental water temperatures has also been emphasized. When the costs of departures from natural thermal regimes are considered, this moves from being an academic exercise to a tangible management imperative. Pest outbreaks of aquatic macroinvertebrates typically occur when environmental conditions disproportionately and overwhelmingly favor a particular species. This has been the case for at least three major river systems of economic importance in South Africa, namely, the Orange River and its major tributary (Vaal River) as well as the Great Fish River. In these rivers, two species of pest blackfly, Simulium chutteri and S. damnosum (Diptera: Simuliidae), have become problematic following on from changes in natural flows through either impoundment or interbasin transfers. In this study, a statistically robust reference thermal condition was defined and exceedances assessed (based on a 2°C increase in water temperatures). The implications of these assessments were then applied to future possible pest blackfly outbreaks, factoring in changes in flows in response to global climate change. Results show that the current seasonal variation in the likelihood of pest outbreaks is replaced by high perpetual outbreak probabilities. Interactions between major environmental variables become synergistic, with major cost implications to regional economies. Lessons from this study can be generalized and used as a means of predicting similar synergistic effects in other aquatic macroinvertebrate disease vectors (such as bilharzia vector snails and mosquitoes) in response to global climate change.

Impact of Population Emigration from Wuhan and Medical Support on COVID-19 Infection in China

BackgroundThe novel Coronavirus Disease 2019 (COVID-19) infection broken out in Wuhan. We aimed to analyse the impact of medical support and population emigration from Wuhan on the cure rate and mortality of COVID-19 infection in China and to provide early warning on the developmental trend of the epidemic.MethodsData were obtained from The National Health Commission of People’s Republic of China, Chinese Center for Disease Control and Prevention and The National Health Commission of People’s Republic of Hubei Province. The Poisson distribution and normal approximate were used to analyse the relationship between population emigration from Wuhan and the probability of outbreaks and to predict the developmental trend of the epidemic situation.ResultsThe outbreak were related to population emigration from Wuhan in 87% of the cities in Hubei. The result of developmental trend indicated that 95% confidence intervals of confirmed case in Xiaogan and HuangGang were 3301.678– 3526.042 and 3201.189–3422.17, respectively. For province outside of Hubei, the outbreak in 76% of the provinces were related to population emigration from Wuhan. Hot spot provinces for epidemic prevention included GuangDong and HeNan. Medical support significantly improved the cure rate of patients with COVID-19 (r = 0.852, p < 0.001).ConclusionPopulation emigration from Wuhan has a certain impact on the probability of outbreaks COVID-19 in Hubei and the whole country, medical support improved the cure rate of patients with COVID-19.