scholarly journals Patterns of spread of influenza A in Canada

2013 ◽  
Vol 280 (1770) ◽  
pp. 20131174 ◽  
Author(s):  
Daihai He ◽  
Jonathan Dushoff ◽  
Raluca Eftimie ◽  
David J. D. Earn
Keyword(s):  
Spatial Patterns ◽  
Generalized Linear Model ◽  
Influenza A ◽  
Absolute Humidity ◽  
Control Measures ◽  
Transmission Model ◽  
Time Period ◽  
Seasonal Epidemics ◽  
Influenza Transmission ◽  
Spatio Temporal

Understanding spatial patterns of influenza transmission is important for designing control measures. We investigate spatial patterns of laboratory-confirmed influenza A across Canada from October 1999 to August 2012. A statistical analysis (generalized linear model) of the seasonal epidemics in this time period establishes a clear spatio-temporal pattern, with influenza emerging earlier in western provinces. Early emergence is also correlated with low temperature and low absolute humidity in the autumn. For the richer data from the 2009 pandemic, a mechanistic mathematical analysis, based on a transmission model, shows that both school terms and weather had important effects on pandemic influenza transmission.

scholarly journals Association of meteorological factors with seasonal activity of influenza A subtypes and B lineages in subtropical western China

2019 ◽  
Vol 147 ◽  
Author(s):  
M. Pan ◽  
H. P. Yang ◽  
J. Jian ◽  
Y. Kuang ◽  
J. N. Xu ◽  
...  
Keyword(s):  
Annual Cycle ◽  
Influenza A ◽  
Meteorological Factors ◽  
Temporal Distribution ◽  
Absolute Humidity ◽  
Influenza Viruses ◽  
Control Measures ◽  
Western China ◽  
The Tropics ◽  
The Impact

AbstractThe seasonality of individual influenza subtypes/lineages and the association of influenza epidemics with meteorological factors in the tropics/subtropics have not been well understood. The impact of the 2009 H1N1 pandemic on the prevalence of seasonal influenza virus remains to be explored. Using wavelet analysis, the periodicities of A/H3N2, seasonal A/H1N1, A/H1N1pdm09, Victoria and Yamagata were identified, respectively, in Panzhihua during 2006–2015. As a subtropical city in southwestern China, Panzhihua is the first industrial city in the upper reaches of the Yangtze River. The relationship between influenza epidemics and local climatic variables was examined based on regression models. The temporal distribution of influenza subtypes/lineages during the pre-pandemic (2006–2009), pandemic (2009) and post-pandemic (2010–2015) years was described and compared. A total of 6892 respiratory specimens were collected and 737 influenza viruses were isolated. A/H3N2 showed an annual cycle with a peak in summer–autumn, while A/H1N1pdm09, Victoria and Yamagata exhibited an annual cycle with a peak in winter–spring. Regression analyses demonstrated that relative humidity was positively associated with A/H3N2 activity while negatively associated with Victoria activity. Higher prevalence of A/H1N1pdm09 and Yamagata was driven by lower absolute humidity. The role of weather conditions in regulating influenza epidemics could be complicated since the diverse viral transmission modes and mechanism. Differences in seasonality and different associations with meteorological factors by influenza subtypes/lineages should be considered in epidemiological studies in the tropics/subtropics. The development of subtype- and lineage-specific prevention and control measures is of significant importance.

Cost-effectiveness of influenza control measures: a dynamic transmission model-based analysis

2013 ◽  
Vol 141 (12) ◽  
pp. 2581-2594 ◽  
Author(s):  
S.-C. CHEN ◽  
C.-M. LIAO
Keyword(s):  
Cost Effectiveness ◽  
Natural Ventilation ◽  
Seasonal Influenza ◽  
School Setting ◽  
Control Measures ◽  
Transmission Model ◽  
Model Based ◽  
Dynamic Transmission Model ◽  
Influenza Transmission ◽  
The Cost

SUMMARYWe investigated the cost-effectiveness of different influenza control strategies in a school setting in Taiwan. A susceptible-exposure-infected-recovery (SEIR) model was used to simulate influenza transmission and we used a basic reproduction number (R0)–asymptomatic proportion (θ) control scheme to develop a cost-effectiveness model. Based on our dynamic transmission model and economic evaluation, this study indicated that the optimal cost-effective strategy for all modelling scenarios was a combination of natural ventilation and respiratory masking. The estimated costs were US$10/year per person in winter for one kindergarten student. The cost for hand washing was estimated to be US$32/year per person, which was much lower than that of isolation (US$55/year per person) and vaccination (US$86/year per person) in containing seasonal influenza. Transmission model-based, cost-effectiveness analysis can be a useful tool for providing insight into the impacts of economic factors and health benefits on certain strategies for controlling seasonal influenza.

SPATIO-TEMPORAL PATTERNS IN A CHOLERA TRANSMISSION MODEL

2015 ◽  
Vol 23 (03) ◽  
pp. 471-484 ◽  
Author(s):  
A. K. MISRA ◽  
MILAN TIWARI ◽  
ANUPAMA SHARMA
Keyword(s):  
Spatial Patterns ◽  
Dimensional Space ◽  
Spatial Dynamics ◽  
Transmission Model ◽  
Cholera Epidemic ◽  
Temporal System ◽  
Spatially Extended ◽  
Variable Population ◽  
Spatio Temporal ◽  
Variable Population Size

Cholera has been a public health threat for centuries. Unlike the biological characteristics, relatively less effort has been paid to comprehend the spatial dynamics of this disease. Therefore, in this paper, we have proposed a cholera epidemic model for variable population size and studied the spatial patterns in two-dimensional space. First, we have performed the equilibrium and local stability analysis of steady states obtained for temporal system. Afterwards, the local and global stability behavior of the endemic steady state in a spatially extended setting has been investigated. The numerical simulations have been done to investigate the spatial patterns. They show that dynamics of the cholera epidemic varies with time and space.

scholarly journals The spatiotemporal characteristics of influenza A and B in the WHO European Region: can one define influenza transmission zones in Europe?

2017 ◽  
Vol 22 (35) ◽  
Author(s):  
Saverio Caini ◽  
Wladimir J Alonso ◽  
Clotilde El-Guerche Séblain ◽  
François Schellevis ◽  
John Paget
Keyword(s):  
Influenza A ◽  
Western Europe ◽  
Control Measures ◽  
World Health ◽  
European Region ◽  
Influenza B ◽  
Influenza Activity ◽  
Influenza Transmission ◽  
Prevention And Control Measures ◽  
Health Organization

We aimed to assess the epidemiology and spatiotemporal patterns of influenza in the World Health Organization (WHO) European Region and evaluate the validity of partitioning the Region into five influenza transmission zones (ITZs) as proposed by the WHO. We used the FluNet database and included over 650,000 influenza cases from 2000 to 2015. We analysed the data by country and season (from July to the following June). We calculated the median proportion of cases caused by each virus type in a season, compared the timing of the primary peak between countries and used a range of cluster analysis methods to assess the degree of overlap between the WHO-defined and data-driven ITZs. Influenza A and B caused, respectively, a median of 83% and 17% cases in a season. There was a significant west-to-east and non-significant (p = 0.10) south-to-north gradient in the timing of influenza activity. Typically, influenza peaked in February and March; influenza A earlier than influenza B. Most countries in the WHO European Region would fit into two ITZs: ‘Western Europe’ and ‘Eastern Europe’; countries bordering Asia may be better placed into extra-European ITZs. Our findings have implications for the presentation of surveillance data and prevention and control measures in this large WHO Region.

scholarly journals Model-based reconstruction of an epidemic using multiple datasets: understanding influenza A/H1N1 pandemic dynamics in Israel

2016 ◽  
Vol 13 (116) ◽  
pp. 20160099 ◽  
Author(s):  
R. Yaari ◽  
G. Katriel ◽  
L. Stone ◽  
E. Mendelson ◽  
M. Mandelboim ◽  
...  
Keyword(s):  
School Children ◽  
Disease Transmission ◽  
Influenza A ◽  
Influenza Pandemic ◽  
Age Groups ◽  
Absolute Humidity ◽  
Transmission Model ◽  
Reproductive Number ◽  
Infection Pattern ◽  
Influenza A H1n1

Intensified surveillance during the 2009 A/H1N1 influenza pandemic in Israel resulted in large virological and serological datasets, presenting a unique opportunity for investigating the pandemic dynamics. We employ a conditional likelihood approach for fitting a disease transmission model to virological and serological data, conditional on clinical data. The model is used to reconstruct the temporal pattern of the pandemic in Israel in five age-groups and evaluate the factors that shaped it. We estimate the reproductive number at the beginning of the pandemic to be R = 1.4. We find that the combined effect of varying absolute humidity conditions and school vacations (SVs) is responsible for the infection pattern, characterized by three epidemic waves. Overall attack rate is estimated at 32% (28–35%) with a large variation among the age-groups: the highest attack rates within school children and the lowest within the elderly. This pattern of infection is explained by a combination of the age-group contact structure and increasing immunity with age. We assess that SVs increased the overall attack rates by prolonging the pandemic into the winter. Vaccinating school children would have been the optimal strategy for minimizing infection rates in all age-groups.

Minimal Transmission in an Influenza A (H3N2) Human Challenge-Transmission Model with Exposure Events in a Controlled Environment

2019 ◽  
Author(s):  
Jonathan S. Nguyen-Van-Tam ◽  
Ben Killingley ◽  
Joanne Enstone ◽  
Michael Hewitt ◽  
Jovan Pantelic ◽  
...  
Keyword(s):  
Influenza A ◽  
Transmission Model ◽  
Controlled Environment

scholarly journals Climate drivers of plague epidemiology in British India, 1898–1949

2020 ◽  
Vol 287 (1928) ◽  
pp. 20200538
Author(s):  
Warren S. D. Tennant ◽  
Mike J. Tildesley ◽  
Simon E. F. Spencer ◽  
Matt J. Keeling
Keyword(s):  
Predictive Models ◽  
Control Measures ◽  
Climate Data ◽  
British India ◽  
Middle Income ◽  
Middle Income Countries ◽  
Complex Interactions ◽  
The World ◽  
Spatio Temporal

Plague, caused by Yersinia pestis infection, continues to threaten low- and middle-income countries throughout the world. The complex interactions between rodents and fleas with their respective environments challenge our understanding of human plague epidemiology. Historical long-term datasets of reported plague cases offer a unique opportunity to elucidate the effects of climate on plague outbreaks in detail. Here, we analyse monthly plague deaths and climate data from 25 provinces in British India from 1898 to 1949 to generate insights into the influence of temperature, rainfall and humidity on the occurrence, severity and timing of plague outbreaks. We find that moderate relative humidity levels of between 60% and 80% were strongly associated with outbreaks. Using wavelet analysis, we determine that the nationwide spread of plague was driven by changes in humidity, where, on average, a one-month delay in the onset of rising humidity translated into a one-month delay in the timing of plague outbreaks. This work can inform modern spatio-temporal predictive models for the disease and aid in the development of early-warning strategies for the deployment of prophylactic treatments and other control measures.

scholarly journals Spatio-temporal evolution and influencing mechanism of the COVID-19 epidemic in Shandong province, China

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jinlong Shi ◽  
Xing Gao ◽  
Shuyan Xue ◽  
Fengqing Li ◽  
Qifan Nie ◽  
...  
Keyword(s):  
Prevention And Control ◽  
Temporal Evolution ◽  
Geographical Location ◽  
Shandong Province ◽  
Control Measures ◽  
Middle Aged ◽  
Health Crisis ◽  
Aged People ◽  
Spatio Temporal ◽  
And Control

AbstractThe novel coronavirus pneumonia (COVID-19) outbreak that emerged in late 2019 has posed a severe threat to human health and social and economic development, and thus has become a major public health crisis affecting the world. The spread of COVID-19 in population and regions is a typical geographical process, which is worth discussing from the geographical perspective. This paper focuses on Shandong province, which has a high incidence, though the first Chinese confirmed case was reported from Hubei province. Based on the data of reported confirmed cases and the detailed information of cases collected manually, we used text analysis, mathematical statistics and spatial analysis to reveal the demographic characteristics of confirmed cases and the spatio-temporal evolution process of the epidemic, and to explore the comprehensive mechanism of epidemic evolution and prevention and control. The results show that: (1) the incidence rate of COVID-19 in Shandong is 0.76/100,000. The majority of confirmed cases are old and middle-aged people who are infected by the intra-province diffusion, followed by young and middle-aged people who are infected outside the province. (2) Up to February 5, the number of daily confirmed cases shows a trend of “rapid increase before slowing down”, among which, the changes of age and gender are closely related to population migration, epidemic characteristics and intervention measures. (3) Affected by the regional economy and population, the spatial distribution of the confirmed cases is obviously unbalanced, with the cluster pattern of “high–low” and “low–high”. (4) The evolution of the migration pattern, affected by the geographical location of Wuhan and Chinese traditional culture, is dominated by “cross-provincial” and “intra-provincial” direct flow, and generally shows the trend of “southwest → northeast”. Finally, combined with the targeted countermeasures of “source-flow-sink”, the comprehensive mechanism of COVID-19 epidemic evolution and prevention and control in Shandong is revealed. External and internal prevention and control measures are also figured out.

scholarly journals High COVID-19 transmission potential associated with re-opening universities can be mitigated with layered interventions

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Ellen Brooks-Pollock ◽  
Hannah Christensen ◽  
Adam Trickey ◽  
Gibran Hemani ◽  
Emily Nixon ◽  
...  
Keyword(s):  
Prediction Interval ◽  
Mitigation Strategies ◽  
Control Measures ◽  
Transmission Model ◽  
Model Parameters ◽  
Infection Rates ◽  
First Year Students ◽  
Transmission Potential ◽  
Plausible Model ◽  
Effective Option

AbstractControlling COVID-19 transmission in universities poses challenges due to the complex social networks and potential for asymptomatic spread. We developed a stochastic transmission model based on realistic mixing patterns and evaluated alternative mitigation strategies. We predict, for plausible model parameters, that if asymptomatic cases are half as infectious as symptomatic cases, then 15% (98% Prediction Interval: 6–35%) of students could be infected during the first term without additional control measures. First year students are the main drivers of transmission with the highest infection rates, largely due to communal residences. In isolation, reducing face-to-face teaching is the most effective intervention considered, however layering multiple interventions could reduce infection rates by 75%. Fortnightly or more frequent mass testing is required to impact transmission and was not the most effective option considered. Our findings suggest that additional outbreak control measures should be considered for university settings.

scholarly journals Detecting Tourism Typologies of Regional Destinations Based on Their Spatio-Temporal and Socioeconomic Performance: A Correlation-Based Complex Network Approach for the Case of Greece

2021 ◽  
Vol 2 (1) ◽  
pp. 113-139
Author(s):  
Dimitrios Tsiotas ◽  
Thomas Krabokoukis ◽  
Serafeim Polyzos
Keyword(s):  
Network Analysis ◽  
Complex Network ◽  
Community Detection ◽  
Optimization Algorithm ◽  
Spatial Dimension ◽  
Economic Dimension ◽  
Complex Network Analysis ◽  
Time Period ◽  
Time Patterns ◽  
Spatio Temporal

Within the context that tourism-seasonality is a composite phenomenon described by temporal, geographical, and socio-economic aspects, this article develops a multilevel method for studying time patterns of tourism-seasonality in conjunction with its spatial dimension and socio-economic dimension. The study aims to classify the temporal patterns of seasonality into regional groups and to configure distinguishable seasonal profiles facilitating tourism policy and development. The study applies a multilevel pattern recognition approach incorporating time-series assessment, correlation, and complex network analysis based on community detection with the use of the modularity optimization algorithm, on data of overnight-stays recorded for the time-period 1998–2018. The analysis reveals four groups of seasonality, which are described by distinct seasonal, geographical, and socio-economic profiles. Overall, the analysis supports multidisciplinary and synthetic research in the modeling of tourism research and promotes complex network analysis in the study of socio-economic systems, by providing insights into the physical conceptualization that the community detection based on the modularity optimization algorithm can enjoy to the real-world applications.

Sign in / Sign up

Export Citation Format

Share Document