scholarly journals Spatio-Temporal Analysis of Influenza-Like Illness and Prediction of Incidence in High-Risk Regions in the United States from 2011 to 2020

2021 ◽  
Vol 18 (13) ◽  
pp. 7120
Author(s):  
Zhijuan Song ◽  
Xiaocan Jia ◽  
Junzhe Bao ◽  
Yongli Yang ◽  
Huili Zhu ◽  
...  
Keyword(s):  
United States ◽  
High Risk ◽  
Moving Average ◽  
Temporal Analysis ◽  
The United States ◽  
Influenza Like Illness ◽  
Important Health ◽  
Control And Prevention ◽  
Spatio Temporal ◽  
Predicted Values

About 8% of the Americans contract influenza during an average season according to the Centers for Disease Control and Prevention in the United States. It is necessary to strengthen the early warning for influenza and the prediction of public health. In this study, Spatial autocorrelation analysis and spatial scanning analysis were used to identify the spatiotemporal patterns of influenza-like illness (ILI) prevalence in the United States, during the 2011–2020 transmission seasons. A seasonal autoregressive integrated moving average (SARIMA) model was constructed to predict the influenza incidence of high-risk states. We found the highest incidence of ILI was mainly concentrated in the states of Louisiana, District of Columbia and Virginia. Mississippi was a high-risk state with a higher influenza incidence, and exhibited a high-high cluster with neighboring states. A SARIMA (1, 0, 0) (1, 1, 0)52 model was suitable for forecasting the ILI incidence of Mississippi. The relative errors between actual values and predicted values indicated that the predicted values matched the actual values well. Influenza is still an important health problem in the United States. The spread of ILI varies by season and geographical region. The peak season of influenza was the winter and spring, and the states with higher influenza rates are concentrated in the southeast. Increased surveillance in high-risk states could help control the spread of the influenza.

scholarly journals Spatio-temporal analysis of influenza-like illness and prediction of incidence in high-risk regions, in the United States from 2011 to 2020

2021 ◽  
Author(s):  
Zhijuan Song ◽  
Xiaocan Jia ◽  
Junzhe Bao ◽  
Yongli Yang ◽  
Huili Zhu ◽  
...  
Keyword(s):  
United States ◽  
Statistical Significance ◽  
Temporal Analysis ◽  
The United States ◽  
Incidence Rates ◽  
Influenza Like Illness ◽  
Temporal Cluster ◽  
Control And Prevention ◽  
Spatio Temporal ◽  
And Control

Abstract Introduction: About 8% of Americans get influenza during an average season from the Centers for Disease Control and Prevention in the United States. It is necessary to strengthen the early warning of influenza and the prediction of public health. Methods In this study, we analyzed the characteristics of Influenza-like Illness (ILI) by Geographic Information System and SARIMA model, respectively. Spatio-temporal cluster analysis detected 23 clusters of ILI during the study period. Results The highest incidence of ILI was mainly concentrated in the states of Louisiana, District of Columbia and Virginia. The Local spatial autocorrelation analysis revealed the High-High cluster was mainly located in Louisiana and Mississippi. This means that if the influenza incidence is high in Louisiana and Mississippi, the neighboring states will also have higher influenza incidence rates. The regression model SARIMA(1, 0, 0)(1, 1, 0)52 with statistical significance was obtained to forecast the ILI incidence of Mississippi. Conclusions The study showed, the ILI incidence will begin to increase in the 45th week 2020 and peak in the 6th week 2021. To conclude, notable epidemiological differences were observed across states, indicating that some states should pay more attention to prevent and control respiratory infectious diseases.

scholarly journals An Automated Approach for Finding Spatio-Temporal Patterns of Seasonal Influenza in the United States: Algorithm Validation Study (Preprint)

2018 ◽  
Author(s):  
Prathyush Sambaturu ◽  
Parantapa Bhattacharya ◽  
Jiangzhuo Chen ◽  
Bryan Lewis ◽  
Madhav Marathe ◽  
...  
Keyword(s):  
United States ◽  
Compression Ratio ◽  
Seasonal Influenza ◽  
Minimum Description Length ◽  
Temporal Patterns ◽  
The United States ◽  
Disease Spread ◽  
Influenza Like Illness ◽  
Incidence Data ◽  
Spatio Temporal

BACKGROUND Agencies such as the Centers for Disease Control and Prevention (CDC) currently release influenza-like illness incidence data, along with descriptive summaries of simple spatio-temporal patterns and trends. However, public health researchers, government agencies, as well as the general public, are often interested in deeper patterns and insights into how the disease is spreading, with additional context. Analysis by domain experts is needed for deriving such insights from incidence data. OBJECTIVE Our goal was to develop an automated approach for finding interesting spatio-temporal patterns in the spread of a disease over a large region, such as regions which have specific characteristics (eg, high incidence in a particular week, those which showed a sudden change in incidence) or regions which have significantly different incidence compared to earlier seasons. METHODS We developed techniques from the area of transactional data mining for characterizing and finding interesting spatio-temporal patterns in disease spread in an automated manner. A key part of our approach involved using the principle of minimum description length for representing a given target set in terms of combinations of attributes (referred to as clauses); we considered both positive and negative clauses, relaxed descriptions which approximately represent the set, and used integer programming to find such descriptions. Finally, we designed an automated approach, which examines a large space of sets corresponding to different spatio-temporal patterns, and ranks them based on the ratio of their size to their description length (referred to as the compression ratio). RESULTS We applied our methods using minimum description length to find spatio-temporal patterns in the spread of seasonal influenza in the United States using state level influenza-like illness activity indicator data from the CDC. We observed that the compression ratios were over 2.5 for 50% of the chosen sets, when approximate descriptions and negative clauses were allowed. Sets with high compression ratios (eg, over 2.5) corresponded to interesting patterns in the spatio-temporal dynamics of influenza-like illness. Our approach also outperformed description by solution in terms of the compression ratio. CONCLUSIONS Our approach, which is an unsupervised machine learning method, can provide new insights into patterns and trends in the disease spread in an automated manner. Our results show that the description complexity is an effective approach for characterizing sets of interest, which can be easily extended to other diseases and regions beyond influenza in the US. Our approach can also be easily adapted for automated generation of narratives.

scholarly journals Prospective Spatial-Temporal Clusters of COVID-19 in Local Communities: Case Study of Kansas City, Missouri, United States

2021 ◽  
Author(s):  
Hadeel AlQadi
Keyword(s):  
United States ◽  
Kansas City ◽  
Temporal Analysis ◽  
The United States ◽  
Local Health ◽  
Test Results ◽  
Large Numbers ◽  
Spatio Temporal ◽  
Spatial Temporal Analysis

Just in the United States (U.S.), the COVID-19 cases reached over 37 million as of August 2021. Kansas City in Missouri State has become one of the major U.S. hot spots for COVID-19 due to an increase in the rate of positive COVID-19 test results. Despite the large numbers of COVID-19 cases in Kansas City, the Spatio-temporal analysis of data has been less investigated. In this study, we conducted a prospective Poisson spatial-temporal analysis of Kansas City, MO, COVID-19 data at the zip code level. The analysis focused on daily COVID-19 cases in four equal periods of three months. We detected temporal patterns of emerging and reemerging space-time clusters between March 2020 and February 202. The statistical results were communicated with local health officials and provided the necessary guidance for decision-making and the allocation of resources.

scholarly journals An Automated Approach for Finding Spatio-Temporal Patterns of Seasonal Influenza in the United States: Algorithm Validation Study

10.2196/12842 ◽  
2020 ◽  
Vol 6 (3) ◽  
pp. e12842
Author(s):  
Prathyush Sambaturu ◽  
Parantapa Bhattacharya ◽  
Jiangzhuo Chen ◽  
Bryan Lewis ◽  
Madhav Marathe ◽  
...  
Keyword(s):  
United States ◽  
Compression Ratio ◽  
Seasonal Influenza ◽  
Minimum Description Length ◽  
Temporal Patterns ◽  
The United States ◽  
Disease Spread ◽  
Influenza Like Illness ◽  
Incidence Data ◽  
Spatio Temporal

Background Agencies such as the Centers for Disease Control and Prevention (CDC) currently release influenza-like illness incidence data, along with descriptive summaries of simple spatio-temporal patterns and trends. However, public health researchers, government agencies, as well as the general public, are often interested in deeper patterns and insights into how the disease is spreading, with additional context. Analysis by domain experts is needed for deriving such insights from incidence data. Objective Our goal was to develop an automated approach for finding interesting spatio-temporal patterns in the spread of a disease over a large region, such as regions which have specific characteristics (eg, high incidence in a particular week, those which showed a sudden change in incidence) or regions which have significantly different incidence compared to earlier seasons. Methods We developed techniques from the area of transactional data mining for characterizing and finding interesting spatio-temporal patterns in disease spread in an automated manner. A key part of our approach involved using the principle of minimum description length for representing a given target set in terms of combinations of attributes (referred to as clauses); we considered both positive and negative clauses, relaxed descriptions which approximately represent the set, and used integer programming to find such descriptions. Finally, we designed an automated approach, which examines a large space of sets corresponding to different spatio-temporal patterns, and ranks them based on the ratio of their size to their description length (referred to as the compression ratio). Results We applied our methods using minimum description length to find spatio-temporal patterns in the spread of seasonal influenza in the United States using state level influenza-like illness activity indicator data from the CDC. We observed that the compression ratios were over 2.5 for 50% of the chosen sets, when approximate descriptions and negative clauses were allowed. Sets with high compression ratios (eg, over 2.5) corresponded to interesting patterns in the spatio-temporal dynamics of influenza-like illness. Our approach also outperformed description by solution in terms of the compression ratio. Conclusions Our approach, which is an unsupervised machine learning method, can provide new insights into patterns and trends in the disease spread in an automated manner. Our results show that the description complexity is an effective approach for characterizing sets of interest, which can be easily extended to other diseases and regions beyond influenza in the US. Our approach can also be easily adapted for automated generation of narratives.

scholarly journals Analysis of lockdown perception in the United States during the COVID-19 pandemic

2021 ◽  
Author(s):  
Francesco Vincenzo Surano ◽  
Maurizio Porfiri ◽  
Alessandro Rizzo
Keyword(s):  
United States ◽  
Temporal Analysis ◽  
The United States ◽  
Economic Effects ◽  
Economic Factors ◽  
Political Orientations ◽  
Containment Measures ◽  
Spatio Temporal ◽  
Different Parts ◽  
Socio Economic Factors

AbstractContainment measures have been applied throughout the world to halt the COVID-19 pandemic. In the United States, several forms of lockdown have been adopted in different parts of the country, leading to heterogeneous epidemiological, social, and economic effects. Here, we present a spatio-temporal analysis of a Twitter dataset comprising 1.3 million geo-localized Tweets about lockdown, from January to May 2020. Through sentiment analysis, we classified Tweets as expressing positive or negative emotions about lockdown, demonstrating a change in perception during the course of the pandemic modulated by socio-economic factors. A transfer entropy analysis of the time series of Tweets unveiled that the emotions in different parts of the country did not evolve independently. Rather, they were mediated by spatial interactions, which were also related to socio-ecomomic factors and, arguably, to political orientations. This study constitutes a first, necessary step toward isolating the mechanisms underlying the acceptance of public health interventions from highly resolved online datasets.

Simulation : The National Security Council

1985 ◽  
Vol 47 ◽  
pp. 3-4
Author(s):  
Douglas W. Simon
Keyword(s):  
United States ◽  
High Risk ◽  
National Security ◽  
Security Council ◽  
The United States ◽  
National Security Council ◽  
Gulf Region ◽  
College Course ◽  
Term Papers ◽  
Global Events

In the spring of 1981 I designed and taught what I considered, at the time, a "high risk" seminar for seventeen junior and senior political science majors. There were to be no textbooks, no lectures, no examinations and no term papers, those hallmarks of the traditional college course. Nevertheless, when the thirteen week course was over, the students were exhausted and claimed that they had never worked so hard in their college careers.The adventure that my students (and I) undertook was a semester long simulation of the United States National Security Council (NSC), dealing with actual global events as they happened. As Washington dealt with a problem, we dealt with the same problem. The simulation was initially offered during the deteriorating situation in Iran and instability in the Gulf region.

scholarly journals Modelling local patterns of child mortality risk: a Bayesian Spatio-temporal analysis

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Alejandro Lome-Hurtado ◽  
Jacques Lartigue-Mendoza ◽  
Juan C. Trujillo
Keyword(s):  
High Risk ◽  
Mexico City ◽  
Child Mortality ◽  
Mortality Risk ◽  
Temporal Analysis ◽  
Health Concern ◽  
The North ◽  
Greater Mexico ◽  
Spatio Temporal ◽  
Child Mortality Rate

Abstract Background Globally, child mortality rate has remained high over the years, but the figure can be reduced through proper implementation of spatially-targeted public health policies. Due to its alarming rate in comparison to North American standards, child mortality is particularly a health concern in Mexico. Despite this fact, there remains a dearth of studies that address its spatio-temporal identification in the country. The aims of this study are i) to model the evolution of child mortality risk at the municipality level in Greater Mexico City, (ii) to identify municipalities with high, medium, and low risk over time, and (iii) using municipality trends, to ascertain potential high-risk municipalities. Methods In order to control for the space-time patterns of data, the study performs a Bayesian spatio-temporal analysis. This methodology permits the modelling of the geographical variation of child mortality risk across municipalities, within the studied time span. Results The analysis shows that most of the high-risk municipalities were in the east, along with a few in the north and west areas of Greater Mexico City. In some of them, it is possible to distinguish an increasing trend in child mortality risk. The outcomes highlight municipalities currently presenting a medium risk but liable to become high risk, given their trend, after the studied period. Finally, the likelihood of child mortality risk illustrates an overall decreasing tendency throughout the 7-year studied period. Conclusions The identification of high-risk municipalities and risk trends may provide a useful input for policymakers seeking to reduce the incidence of child mortality. The results provide evidence that supports the use of geographical targeting in policy interventions.

Developing National Genotype-Independent Indicators for Recent Mycobacterium Tuberculosis Transmission Using Pediatric Cases—United States, 2011-2017

2021 ◽  
pp. 003335492098521
Author(s):  
Alexia V. Harrist ◽  
Clinton J. McDaniel ◽  
Jonathan M. Wortham ◽  
Sandy P. Althomsons
Keyword(s):  
United States ◽  
Mycobacterium Tuberculosis ◽  
Statistical Model ◽  
Moving Average ◽  
The United States ◽  
Tuberculosis Transmission ◽  
Average Proportion ◽  
Pediatric Tb ◽  
Case Rate ◽  
Pediatric Tuberculosis

Introduction Pediatric tuberculosis (TB) cases are sentinel events for Mycobacterium tuberculosis transmission in communities because children, by definition, must have been infected relatively recently. However, these events are not consistently identified by genotype-dependent surveillance alerting methods because many pediatric TB cases are not culture-positive, a prerequisite for genotyping. Methods We developed 3 potential indicators of ongoing TB transmission based on identifying counties in the United States with relatively high pediatric (aged <15 years) TB incidence: (1) a case proportion indicator: an above-average proportion of pediatric TB cases among all TB cases; (2) a case rate indicator: an above-average pediatric TB case rate; and (3) a statistical model indicator: a statistical model based on a significant increase in pediatric TB cases from the previous 8-quarter moving average. Results Of the 249 US counties reporting ≥2 pediatric TB cases during 2009-2017, 240 and 249 counties were identified by the case proportion and case rate indicators, respectively. The statistical model indicator identified 40 counties with a significant increase in the number of pediatric TB cases. We compared results from the 3 indicators with an independently generated list of 91 likely transmission events involving ≥2 pediatric cases (ie, known TB outbreaks or case clusters with reported epidemiologic links). All counties with likely transmission events involving multiple pediatric cases were identified by ≥1 indicator; 23 were identified by all 3 indicators. Practice Implications This retrospective analysis demonstrates the feasibility of using routine TB surveillance data to identify counties where ongoing TB transmission might be occurring, even in the absence of available genotyping data.

Sign in / Sign up

Export Citation Format

Share Document