Patterns Of Outbreaks Research Articles

Exponential Damping: The Key to Successful Containment of COVID-19.

Due to its excessive capacity for human-to-human transmission, the 2019 coronavirus disease (COVID-19) has now been declared a global public health emergency. Here we propose a simple model based on exponential infectious growth, but with a time-varying, largely damping, transmission rate. This model provides an excellent fit to the existing data for 46 countries with 10,000+ cases by 16 May 2020, five continents and the entire world. Hence, the model has largely captured the transmission patterns of the COVID-19 outbreak under a variety of intervention and control measures. The damping rate ranged from −0.0228 to 0.1669 d−1 globally (a negative damping rate represents acceleration in spread) and can greatly affect the duration of the outbreak and the eventual number of infections. Our model suggests that it is possible to defeat the COVID-19 pandemic by the end of 2020 through achieving a high damping rate (0.0615 d−1). However, the global damping rate is rather low (0.0504 d−1 before 26 April) and has dropped even further since late April (0.0168 d−1). Easing currently implemented control measures in countries with weak or no damping in transmission could lead to an exponential rebound of COVID-19 spread.

Asynchronicity of endemic and emerging mosquito-borne disease outbreaks in the Dominican Republic

Mosquito-borne viruses threaten the Caribbean due to the region’s tropical climate and seasonal reception of international tourists. Outbreaks of chikungunya and Zika have demonstrated the rapidity with which these viruses can spread. Concurrently, dengue fever cases have climbed over the past decade. Sustainable disease control measures are urgently needed to quell virus transmission and prevent future outbreaks. Here, to improve upon current control methods, we analyze temporal and spatial patterns of chikungunya, Zika, and dengue outbreaks reported in the Dominican Republic between 2012 and 2018. The viruses that cause these outbreaks are transmitted by Aedes mosquitoes, which are sensitive to seasonal climatological variability. We evaluate whether climate and the spatio-temporal dynamics of dengue outbreaks could explain patterns of emerging disease outbreaks. We find that emerging disease outbreaks were robust to the climatological and spatio-temporal constraints defining seasonal dengue outbreak dynamics, indicating that constant surveillance is required to prevent future health crises.

Chikungunya Arbovirus in Bangladesh: An Epidemic with Medical Entomology

Background: Chikungunya virus (CHIKV) transmits to humans by infected mosquitoes of Aedes Aegypti and Aedes Albopictus that has been emerged as a serious public health threat globally. In 2017, Dhaka city of Bangladesh experienced an explosive outbreak of chikungunya, an arthropod-borne virus resulting in more than 13,800 clinically confirmed cases. This paper focuses on the pattern of massive outbreak of chikungunya in Dhaka City as well as the association between different entomological indicators and chikungunya infection. Methods: The relevant patient information was principally collected from the Institute of Epidemiology, Disease Control and Research (IEDCR) and Dhaka Medical College and Hospital (DMCH), and entomological surveillance data from the Communicable Disease Control (CDC) of Directorate General of Health Services (DGHS) in Dhaka. In addition, surveys were conducted in localities where an outbreak of CHIKV had occurred. A total of 2708 households were visited and a total of 4767 habitats were characterized. Face-to- face interview process was also employed from 436 confirmed patients. The regression analysis, Odds Ratio (OR), and Relative Risk (RR) were used to analyse the risk factors of entomological indicators in making connotation with chikungunya infection. Spatial mapping with proportional symbology and geostatistics were also employed. Results: The overall chikungunya outbreak in 2017 for Dhaka city shows that about 137 per 100,000 population were infected with CHIKV with the average prevalence rate of 1.11±0.65% (95% CI of 0.97- 1.25). Some 97.3% patients had high-grade fever with an average maximum temperature of 103.9±0.78°F, 88.3% patients declared for deep and sharp myalgia, and almost all the patients (98.6%) reported severe arthralgia in different joints. In addition, the average odds of confirmed chikungunya patients in households were found to be 2.43 times (OR: 2.24-2.70 with 95% CI and P <0.001) and the relative risk was calculated for 2.07 (RR: 1.91-2.24 with 95% CI and P <0.001) times compared to the infections in negative households in Dhaka City. Moreover, the entomological information shows that out of 4767 habitats, some 12.96% were positive for Aedes Aegypti larvae and pupae, and positive correlations were found between different entomological indices and chikungunya infections. Conclusion: Since there is no curative treatment for chikungunya, a proper policy including patient management and Behaviour Change Communication (BCC) as well as strong surveillance strategies should be formulated for the prevention of chikungunya infection as well as to lessen the incidence of mosquito vectors.

Time Trend of Swine Flu at a Tertiary Care Centre of Western Rajasthan: A Decadal Analysis

Background: Swine flu had been established as seasonal influenza, but antigenic variation continues to result in epidemics that necessitate continued surveillance. Present study aims to describe the time trend of swine flu from its initiation in 2009 to 2019 from western zone of Rajasthan.
 Material and Methods: This secondary analysis of swine flu data from established seasonal disease reporting was conducted at a tertiary care centre of western Rajasthan which was the only swine flu testing centre in this area. Only Lab confirmed cases from this centre were included for month wise distribution.
 Results: First case from Jodhpur was seen in August 2009. Large peaked outbreaks were seen around every 4 years namely 2009-10, 2014-15, 2018-19 corresponding to the antigenic variation of this virus namely California, Michigan and Brisbane strain respectively. Cases were seen in mostly in winters with peak in December/January. Mortality rate among these lab confirmed cases varied from 0–22% in different outbreaks.
 Conclusion: Continued laboratory surveillance is necessary to identify the time pattern of outbreaks and for early identification of emergence of new strain to ensure early and timely preparedness for impending epidemics.

Clinical efficacy of respiratory virus detection by using the FilmArray method in children admitted with respiratory infection

Purpose: Respiratory virus infection is a common cause of hospitalization in children. Rapid testing for respiratory viruses, such as the FilmArray method, can be clinically useful. However, insufficient evidence exists to support its use in standard clinical care. Methods: We retrospectively analyzed data from children under 18 years old who received the multiplex real-time polymerase chain reaction array (multiplex RT-PCR) method in 2017 and by FilmArray respiratory panel (FilmArray RP) in 2018. Results: Between January, 2017 and December, 2018, we reviewed data from 1,480 hospitalized children. The number of children with virus detection in respiratory viral PCR was 523 in the multiplex RT-PCR method and 419 in the FilmArray method. Seasonal vi rus outbreak patterns were similar to those of Korea Centers for Disease Control and Prevention in both groups. There was no differ ence between the 2 groups in the mean length of hospital stay. The time from admission to isolation by influenza infection was sig nificantly shorter in the FilmArray group than in the multiplex RT-PCR group among patients who were not diagnosed with influ enza infection by rapid antigen test at the time of admission. Conclusion: The use of FilmArray method for respiratory viruses did not diminish length of hospital stay. However, the FilmArray method may quickly detect the prevalence of respiratory infection and aid in clinical treatment. In addition, it was related with a re duced time from admission to isolation by influenza infection in hospitalized children who were not identified with influenza infec tion by rapid antigen test at the time of admission. (Allergy Asthma Respir Dis 2021;9:12-20)

Analysing Spatial Patterns of the COVID-19 Outbreak in Turkey

COVID-19 is first detected on 12 March 2020 in Turkey, and since that day more than 100 thousand people are infected. In this study, we aim to determine risky provinces in terms of COVID-19 outbreak and also explore the spatial dynamics of the outbreak in Turkey using province-level data. To analyze spatial patterns of COVID-19, we employ spatial dependence statistics Moran-I. Also, we employ Local Indicator Spatial Association-LISA to detect the hot-spots and cold-spots. Moran-I coefficient found as low and statistically significant that shows spatial interaction is not strong in the context of the whole country. Also using LISA, we found Düzce, Kocaeli, Ordu, Tekirdağ, and Trabzon as hot-spots for data period, which indicates these cities can be classified as risky in terms of COVID-19 outbreak. There are more spatial interaction with their neighbours cities. In terms of the COVID-19 variable, in hot-spot provinces and neighboring provinces of these provinces, measures should be intensified, and control should be increased.

Science in mobile apps for education: tackling online learning within pandemic outbreak

In adapting the issues of pandemic, educational sectors in Malaysia from the lower school level till university levels are encouraged to forecast any specific tools that may help in cater the needs in utilizing online learning. It is by right the issues that been face by global outbreak for everyone on earth as Covid19 that embarked big challenges in face to face and normal daily classes and learning method. Therefore this paper is written to give a general overview, uncover the possible models in used and technologies that may overcome the needs of online education specifically focused for improving the usage of online educational tools especially with the use of mobile ap-plication for learning itself. The concept of manual for pen and paper reporting can makes the reporting very slow especially in terms of communication and sharing of information. Plus, within the outbreak patterns may will provide inaccurate information and may also late results to be achieved. This paper is discussing model in plan and the implementation proposed with overcome results of the post results questionnaire conducted to get the major feedback on usage of the current proposed app.

Containment measures limit environmental effects on COVID-19 early outbreak dynamics

Environmental factors are well known to affect spatio-temporal patterns of infectious disease outbreaks, but whether the rapid spread of COVID-19 across the globe is related to local environmental conditions is highly debated. We assessed the impact of environmental factors (temperature, humidity and air pollution) on the global patterns of COVID-19 early outbreak dynamics during January–May 2020, controlling for several key socio-economic factors and airport connections. We showed that during the earliest phase of the global outbreak (January–March), COVID-19 growth rates were non-linearly related to climate, with fastest spread in regions with a mean temperature of ca. 5 °C, and in the most polluted regions. However, environmental effects faded almost completely when considering later outbreaks, in keeping with the progressive enforcement of containment actions. Accordingly, COVID-19 growth rates consistently decreased with stringent containment actions during both early and late outbreaks. Our findings indicate that environmental drivers may have played a role in explaining the early variation among regions in disease spread. With limited policy interventions, seasonal patterns of disease spread might emerge, with temperate regions of both hemispheres being most at risk of severe outbreaks during colder months. Nevertheless, containment measures play a much stronger role and overwhelm impacts of environmental variation, highlighting the key role for policy interventions in curbing COVID-19 diffusion within a given region. If the disease will become seasonal in the next years, information on environmental drivers of COVID-19 can be integrated with epidemiological models to inform forecasting of future outbreak risks and improve management plans.

Understanding the spatio-temporal pattern of COVID-19 outbreak in India using GIS and India's response in managing the pandemic.

Due to the outbreak of Coronavirus, humans all over the world are facing several health problems. The present study has explored the spatio‐temporal pattern of Coronavirus spread in India including spatial clustering, identification of hotspot, spatial heterogeneity, and homogeneity, spatial trend, and direction of COVID‐19 cases using spatial statistical analysis during the period of 30 January to 20 June 2020. Besides, the polynomial regression model has been used for predictions of COVID‐19 affected population and related deaths. The study found positive spatial heterogeneity in COVID‐19 cases in India. The study has also identified 17 epicentres across the country with high incidence rates. The directional distribution of ellipse polygon shows that the spread of COVID‐19 now trending towards the east but the concentration of cases is mainly in the western part of the country. The country's trend of COVID‐19 follows a fourth‐order polynomial growth and is characterized by an increasing trend. The prediction results show that as on 14 October India will reach 14,660,400 COVID‐19 cases and the death toll will cross 152,945. Therefore, a “space‐specific” policy strategy would be a more suitable strategy for reducing the spatial spread of the virus in India. Moreover, the study has broadly found out seven sectors, where the Government of India lacks in terms of confronting the ongoing pandemic. The study has also recommended some appropriate policies which would be immensely useful for the administration to initiate strategic planning.

Different pattern of the second outbreak of COVID-19 in Marseille, France

ObjectiveTo describe the characteristics of COVID-19 patients seen in March-April and June-August 2020 in Marseille, France with the aim to investigate possible changes in the disease between these two time periods. MethodsDemographics, hospitalization rate, transfer to intensive care unit (ICU), lethality, clinical and biological parameters were investigated. ResultsCompared to those seen in March-April, COVID-19 patients seen in June-August were significantly younger (39.2 vs. 45.3 years), more likely to be male (52.9% vs. 45.6%), and less likely to be hospitalized (10.7 vs. 18.0%), to be transferred to ICU (0.9% vs. 1.8%) and to die (0.1% vs. 1.1%). Their mean fibrinogen and D-dimer blood levels were lower (1.0 vs. 1.5 g/L and 0.6 vs. 1.1 μg/mL, respectively). By contrast, their viral load was higher (cycle threshold ≤16 = 5.1% vs. 3.7%). ConclusionsPatients in the two periods did not present marked age and sex differences, but markers of severity were undoubtedly less prevalent in the summer period, associating with a 10 times decrease in the lethality rate.

Spatial Patterns of Anthrax Outbreaks and Cases among Livestock in Lesotho, 2005-2016.

Background: Although anthrax occurs globally, the burden of the disease remains particularly high in Africa. Furthermore, the disease anthrax has significant public health and economic implications. However, sufficient attention has not been given to the geographic distribution of anthrax outbreaks and cases in Lesotho. Therefore, this study investigates the spatial patterns of anthrax outbreaks and cases among livestock in Lesotho from 2005 to 2016. Methods: A cross-sectional study design was adopted to realise the objectives of this study using retrospective data of anthrax outbreaks and cases recorded by the Department of Livestock Services (DLS) between 2005 and 2016. Anthrax outbreaks were geo-coded at village level and aggregated at district level. Proportions and 95% CI of anthrax outbreaks and cases by village and district were calculated. Cartographic maps displaying the distribution of anthrax outbreaks and cases at village and district level were constructed. Results: A total of 38 outbreaks were reported over the study period, and they were clustered in the Lowlands districts of Lesotho. Most outbreaks (52.6%, 20/38) in livestock were reported in the Maseru district. The Leribe district reported the lowest proportions of outbreaks (5.3%, 2/38) and cases (0.6%, 3/526). At the village level, 18% (7/38) of outbreaks were in Maseru Urban, followed by Ratau (16%, 6/38) and Mofoka (13%, 5/38). The Maseru district reported the highest (1.3%, 369/29,070) proportion of cases followed by Mafeteng (0.9%, 73/8530). The village with the most cases was Kolo (10.5%, 21/200), followed by Thaba-Chitja (7.7%, 33/430). Conclusion: Anthrax outbreaks and cases exclusively occur in the Lowlands districts of Lesotho, with villages such as Mahobong, Pitseng, Kolo, and Thaba-Chitja having a higher risk of anthrax disease. Findings of the present study have serious public health implications in light of the fact that between 2003 and 2008 Lesotho’s main abattoir was closed; hence, most of the meat in Lesotho was imported and/or sourced from the informal slaughter facilities. Much larger studies are needed to further investigate factors contributing to spatial disparities in anthrax outbreaks and cases observed in this study. Findings of the present study can be used to guide the formulation of a policy on prevention and control of anthrax in Lesotho.

Patterns of COVID-19 testing and mortality by race and ethnicity among United States veterans: Anationwide cohort study.

There is growing concern that racial and ethnic minority communities around the world are experiencing a disproportionate burden of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and coronavirus disease 2019 (COVID-19). We investigated racial and ethnic disparities in patterns of COVID-19 testing (i.e., who received testing and who tested positive) and subsequent mortality in the largest integrated healthcare system in the United States. This retrospective cohort study included 5,834,543 individuals receiving care in the US Department of Veterans Affairs; most (91%) were men, 74% were non-Hispanic White (White), 19% were non-Hispanic Black (Black), and 7% were Hispanic. We evaluated associations between race/ethnicity and receipt of COVID-19 testing, a positive test result, and 30-day mortality, with multivariable adjustment for a wide range of demographic and clinical characteristics including comorbid conditions, health behaviors, medication history, site of care, and urban versus rural residence. Between February 8 and July 22, 2020, 254,595 individuals were tested for COVID-19, of whom 16,317 tested positive and 1,057 died. Black individuals were more likely to be tested (rate per 1,000 individuals: 60.0, 95% CI 59.6-60.5) than Hispanic (52.7, 95% CI 52.1-53.4) and White individuals (38.6, 95% CI 38.4-38.7). While individuals from minority backgrounds were more likely to test positive (Black versus White: odds ratio [OR] 1.93, 95% CI 1.85-2.01, p < 0.001; Hispanic versus White: OR 1.84, 95% CI 1.74-1.94, p < 0.001), 30-day mortality did not differ by race/ethnicity (Black versus White: OR 0.97, 95% CI 0.80-1.17, p = 0.74; Hispanic versus White: OR 0.99, 95% CI 0.73-1.34, p = 0.94). The disparity between Black and White individuals in testing positive for COVID-19 was stronger in the Midwest (OR 2.66, 95% CI 2.41-2.95, p < 0.001) than the West (OR 1.24, 95% CI 1.11-1.39, p < 0.001). The disparity in testing positive for COVID-19 between Hispanic and White individuals was consistent across region, calendar time, and outbreak pattern. Study limitations include underrepresentation of women and a lack of detailed information on social determinants of health. In this nationwide study, we found that Black and Hispanic individuals are experiencing an excess burden of SARS-CoV-2 infection not entirely explained by underlying medical conditions or where they live or receive care. There is an urgent need to proactively tailor strategies to contain and prevent further outbreaks in racial and ethnic minority communities.

Interactive Effects of Copper Pipe, Stagnation, Corrosion Control, and Disinfectant Residual Influenced Reduction of Legionella pneumophila during Simulations of the Flint Water Crisis.

Flint, MI experienced two outbreaks of Legionnaires’ Disease (LD) during the summers of 2014 and 2015, coinciding with use of Flint River as a drinking water source without corrosion control. Using simulated distribution systems (SDSs) followed by stagnant simulated premise (i.e., building) plumbing reactors (SPPRs) containing cross-linked polyethylene (PEX) or copper pipe, we reproduced trends in water chemistry and Legionella proliferation observed in the field when Flint River versus Detroit water were used before, during, and after the outbreak. Specifically, due to high chlorine demand in the SDSs, SPPRs with treated Flint River water were chlorine deficient and had elevated L. pneumophila numbers in the PEX condition. SPPRs with Detroit water, which had lower chlorine demand and higher residual chlorine, lost all culturable L. pneumophila within two months. L. pneumophila also diminished more rapidly with time in Flint River SPPRs with copper pipe, presumably due to the bacteriostatic properties of elevated copper concentrations caused by lack of corrosion control and stagnation. This study confirms hypothesized mechanisms by which the switch in water chemistry, pipe materials, and different flow patterns in Flint premise plumbing may have contributed to observed LD outbreak patterns.

Spatial analysis of the COVID-19 distribution pattern in São Paulo State, Brazil.

At the end of 2019, the outbreak of COVID-19 was reported in Wuhan, China. The outbreak spread quickly to several countries, becoming a public health emergency of international interest. Without a vaccine or antiviral drugs, control measures are necessary to understand the evolution of cases. Here, we report through spatial analysis the spatial pattern of the COVID-19 outbreak. The study site was the State of São Paulo, Brazil, where the first case of the disease was confirmed. We applied the Kernel Density to generate surfaces that indicate where there is higher density of cases and, consequently, greater risk of confirming new cases. The spatial pattern of COVID-19 pandemic could be observed in São Paulo State, in which its metropolitan region standed out with the greatest cases, being classified as a hotspot. In addition, the main highways and airports that connect the capital to the cities with the highest population density were classified as medium density areas by the Kernel Density method.It indicates a gradual expansion from the capital to the interior. Therefore, spatial analyses are fundamental to understand the spread of the virus and its association with other spatial data can be essential to guide control measures.

Estimating the Impact of Daily Weather on the Temporal Pattern of COVID-19 Outbreak in India

The COVID-19 pandemic has spread obstreperously in India. The increase in daily confirmed cases accelerated significantly from ~ 5 additional new cases (ANC)/day during early March up to ~ 249 ANC/day during early June. An abrupt change in this temporal pattern was noticed during mid-April, from which can be inferred a much reduced impact of the nationwide lockdown in India. Daily maximum (TMax), minimum (TMin), mean (TMean) and dew point temperature (TDew), wind speed (WS), relative humidity, and diurnal range in temperature and relative humidity during March 01 to June 04, 2020 over 9 major affected cities are analyzed to look into the impact of daily weather on COVID-19 infections on that day and 7, 10, 12, 14, 16 days before those cases were detected (i.e., on the likely transmission days). Spearman’s correlation exhibits significantly lower association with WS, TMax, TMin, TMean, TDew, but is comparatively better with a lag of 14 days. Support Vector regression successfully estimated the count of confirmed cases (R2 > 0.8) at a lag of 12–16 days, thus reflecting a probable incubation period of 14 ± 02 days in India. Approximately 75% of total cases were registered when TMax, TMean, TMin, TDew, and WS at 12–16 days previously were varying within the range of 33.6–41.3 °C, 29.8–36.5 °C, 24.8–30.4 °C, 18.7–23.6 °C, and 4.2–5.75 m/s, respectively. Thus, we conclude that coronavirus transmission is not well correlated (linearly) with any individual weather parameter; rather, transmission is susceptible to a certain weather pattern. Hence multivariate non-linear approach must be employed instead.

Disparity in HIV Service Interruption in the Outbreak of COVID-19 in South Carolina.

To examine HIV service interruptions during the COIVD-19 outbreak in South Carolina (SC) and identify geospatial and socioeconomic correlates of such interruptions, we collected qualitative, geospatial, and quantitative data from 27 Ryan White HIV clinics in SC in March, 2020. HIV service interruptions were categorized (none, minimal, partial, and complete interruption) and analyzed for geospatial heterogeneity. Nearly 56% of the HIV clinics were partially interrupted and 26% were completely closed. Geospatial heterogeneity of service interruption existed but did not exactly overlap with the geospatial pattern of COVID-19 outbreak. The percentage of uninsured in the service catchment areas was significantly correlated with HIV service interruption (F = 3.987, P = .02). This mixed-method study demonstrated the disparity of HIV service interruptions in the COVID-19 in SC and suggested a contribution of existing socioeconomic gaps to this disparity. These findings may inform the resources allocation and future strategies to respond to public health emergencies.

Forest landscape structure influences the cyclic‐eruptive spatial dynamics of forest tent caterpillar outbreaks

AbstractA fundamental question in forest insect ecology is the role of forest landscape structure, particularly the amount and spatial configuration of host tree species, in shaping the dynamics of recurring forest insect outbreaks. For forest tent caterpillar (FTC), independent studies do not converge on a singular conclusion, although all indicate that forest structure influences outbreak dynamics. These studies also vary in how they treat climate as a covariate. We evaluated the relative importance of host forest landscape composition and configuration, as well as climate, for their influence on FTC outbreak cycling in the twentieth century. We predicted that FTC outbreaks would exhibit greater synchrony and intensity within areas associated with higher abundance of host trees. We reconstructed FTC outbreaks from 1928 to 2006 using tree‐ring analysis within a well‐structured experimental landscape located in northwestern Ontario and northern Minnesota. Time‐series clustering and spatial nonparametric covariance were used to determine whether similarities in time series and patterns in spatial synchrony corresponded with land management history. Using constrained ordination, we compared statistical properties of outbreak time series to landscape variables representing host abundance, forest configuration, and climate. We found no evidence of climatic effects at the scale of this study, but a significant albeit small influence of landscape structure on outbreak dynamics. Outbreaks were more synchronous and more cyclic within managed zones containing a greater relative abundance of aspen and other hardwood host tree species, compared with the more conifer‐dominated Wilderness area. Yet, we also observed asynchronous outbreak dynamics across the study area, such that correlations with slower‐changing forest landscape variables varied starkly among outbreak pulses. Consequently, the strength of relationship between landscape variables and FTC outbreak patterns varied strongly through time—a result that may explain why short‐term studies yield conclusions that are at odds with one another. Our results speak to the importance of long time series, contrasting landscape structure, use of multivariate methods, and controlling for climatic variation when investigating the effects of forest landscape structure on the cyclic‐eruptive spatial dynamics for forest defoliators.

Migration sources and pathways of the pest species Sogatella furcifera in Yunnan, China, and across the border inferred from DNA and wind analyses.

The migration sources and pathways of Sogatella furcifera (Horváth) in topologically complex regions like Yunnan, China, and adjacent montane areas have long been a challenging task and a bottleneck in effective pest forecast and control. The present research reinvestigated this issue using a combination of mtDNA and long‐term historical wind field data in an attempt to provide new insights. Genetic analyses showed that the 60 populations of S. furcufera collected across Myanmar, Thailand, Laos, Vietnam, Yunnan, Guizhou, and Sichuan lack genetic structure and geographic isolation, while spatial analysis of haplotype and diversity indices discovered geographic relevance between populations. Migration rate analysis combined with high‐resolution 10‐year wind field analysis detected the following migration sources, pathways, and impacted areas which could explain the outbreak pattern in Yunnan. (a) Dominating stepwise northward migrations originated from northern Indochina, southern Yunnan, and central‐eastern Yunnan, impacting their northern areas. (b) Concurring summer–autumn southward (return) migration originated from nearly all latitude belts of Sichuan and Yunnan mainly impacting central and southern Yunnan. (c) Regular eastward and summer–autumn westward migrations across Yunnan. The northward migration reflects the temporal rhythm of gradual outbreaks from the south to the north in a year, while the return migration may explain the repeated or very severe outbreaks in the impacted areas. To form a better pest forecast and control network, attention must also be paid to the northern part of Yunnan to suppress the impact of return migration in summers and autumns.

Forecasting seasonal influenza-like illness in South Korea after 2 and 30 weeks using Google Trends and influenza data from Argentina.

We aimed to identify variables for forecasting seasonal and short-term targets for influenza-like illness (ILI) in South Korea, and other input variables through weekly time-series of the variables. We also aimed to suggest prediction models for ILI activity using a seasonal autoregressive integrated moving average, including exogenous variables (SARIMAX) models. We collected ILI, FluNet surveillance data, Google Trends (GT), weather, and air-pollution data from 2010 to 2019, applying cross-correlation analysis to identify the time lag between the two respective time-series. The relationship between ILI in South Korea and the input variables were evaluated with Linear regression models. To validate selected input variables, the autoregressive moving average, including exogenous variables (ARMAX) models were used to forecast seasonal ILI after 2 and 30 weeks with a three-year window for the training set used in the fixed rolling window analysis. Moreover, a final SARIMAX model was constructed. Influenza A virus activity peaks in South Korea were roughly divided between the 51st and the 7th week, while those of influenza B were divided between the 3rd and 14th week. GT showed the highest correlation coefficient with forecasts from a week ahead, and seasonal influenza outbreak patterns in Argentina showed a high correlation with those 30 weeks ahead in South Korea. The prediction models after 2 and 30 weeks using ARMAX models had R2 values of 0.789 and 0.621, respectively, indicating that reference models using only the previous seasonal ILI could be improved. The currently eligible input variables selected by the cross-correlation analysis helped propose short-term and long-term predictions for ILI in Korea. Our findings indicate that influenza surveillance in Argentina can help predict seasonal ILI patterns after 30 weeks in South Korea, and these can help the Korea Centers for Disease Control and Prevention determine vaccine strategies for the next ILI season.

The patterns of seasonal outbreaks of acute respiratory diseases

The COVID-19 epidemic cause is considered to be "patient zero" contaminated by coronavirus infection in December 2019 in China. However, accumulated facts dispute this version of events. The first infected patient appeared in September in the United States, in December - in France. in November-December pneumonia of similar severity raged in Russia. It is difficult to explain numerous outbreaks of simultaneous contagions in isolated staffs, including military garrisons. In most of healthy adults' antibodies to coronavirus are found. The most of infected patients have no symptoms of disease. To explain these paradoxes ecological hypothesis is proposed - humanity and coronaviruses have been interacting evolutionarily for thousands years in system of biogeocenosis, periodically confronting under influence of cyclical ecological processes. The purpose of study is to investigate relationship between mortality of acute out-of-hospital pneumonia, coronavirus ones included, and cyclical fluctuations of environmental conditions. The primary material was obtained from data of Rosstat, international information bases, results of scientific studies. The classic methods of medical statistics were applied. The summer solstice (June) was used as reference point for estimating dynamics of monthly mortality in 2015-2020. The analysis of dynamics of mortality of pneumonia averaged for 2015-2020 revealed three epidemic waves with peaks in October, January, and March. Such cyclical pattern was consistently reproduced for particular years in 2015-2020. The first wave always was the lowest one. When the second wave exceeds the third one, increase in mortality in February and March was the smallest one. The greatest epidemic losses were established at the plateau. In 2020, the rate of coronavirus infection in the world coincides with the established cyclicity. The stable reproducibility of three peaks of mortality in same months testifies their regular character. The cause of all this may be the cyclicity of ecological interactions between coronaviruses and human population.