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

Author(s):  
Zhijuan Song ◽  
Xiaocan Jia ◽  
Junzhe Bao ◽  
Yongli Yang ◽  
Huili Zhu ◽  
...  

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.

Author(s):  
Zhijuan Song ◽  
Xiaocan Jia ◽  
Junzhe Bao ◽  
Yongli Yang ◽  
Huili Zhu ◽  
...  

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.


2018 ◽  
Author(s):  
Prathyush Sambaturu ◽  
Parantapa Bhattacharya ◽  
Jiangzhuo Chen ◽  
Bryan Lewis ◽  
Madhav Marathe ◽  
...  

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.


2021 ◽  
Author(s):  
Hadeel AlQadi

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.


2013 ◽  
Vol 33 (8) ◽  
pp. 886-893 ◽  
Author(s):  
GS Goldman ◽  
PG King

Background: There is increasing evidence that herpes zoster (HZ) incidence rates among children and adults (aged <60 years) with a history of natural varicella are influenced primarily by the frequency of exogenous exposures, while asymptomatic endogenous reactivations help to cap the rate at approximately 550 cases/100,000 person-years when exogenous boosting becomes rare. The Antelope Valley Varicella Active Surveillance Project was funded by the Centers for Disease Control and Prevention in 1995 to monitor the effects of varicella vaccination in one of the three representative regions of the United States. The stability in the data collection and number of reporting sites under varicella surveillance from 1995–2002 and HZ surveillance during 2000–2001 and 2006–2007 contributed to the robustness of the discerned trends. Discussion: Varicella vaccination may be useful for leukemic children; however, the target population in the United States is all children. Since the varicella vaccine inoculates its recipients with live, attenuated varicella–zoster virus (VZV), clinical varicella cases have dramatically declined. Declining exogenous exposures (boosts) from children shedding natural VZV have caused waning cell-mediated immunity. Thus, the protection provided by varicella vaccination is neither lifelong nor complete. Moreover, dramatic increases in the incidence of adult shingles cases have been observed since HZ was added to the surveillance in 2000. In 2013, this topic is still debated and remains controversial in the United States. Summary: When the costs of the booster dose for varicella and the increased shingles recurrences are included, the universal varicella vaccination program is neither effective nor cost-effective.


10.2196/12842 ◽  
2020 ◽  
Vol 6 (3) ◽  
pp. e12842
Author(s):  
Prathyush Sambaturu ◽  
Parantapa Bhattacharya ◽  
Jiangzhuo Chen ◽  
Bryan Lewis ◽  
Madhav Marathe ◽  
...  

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.


Author(s):  
Francesco Vincenzo Surano ◽  
Maurizio Porfiri ◽  
Alessandro Rizzo

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.


2021 ◽  
Author(s):  
Hawre Jalal ◽  
Kyueun Lee ◽  
Donald S. Burke

Better understanding of the spatiotemporal structure of the COVID-19 epidemic in the USA may help inform more effective prevention and control strategies. By analyzing daily COVID-19 case data in the United States, Mexico and Canada, we found four continental-scale epidemic wave patterns, including travelling waves, that spanned multiple state and even international boundaries. These major epidemic patterns co-varied strongly with continental-scale seasonal temperature change patterns. Geo-contiguous states shared similar timing and amplitude of epidemic wave patterns irrespective of similarities or differences in state government political party affiliations. These analyses provide evidence that seasonal factors, probably weather changes, have exerted major effects on local COVID-19 incidence rates. Seasonal wave patterns observed during the first year of the epidemic may become repeated in the subsequent years.


Blood ◽  
2010 ◽  
Vol 116 (21) ◽  
pp. 873-873
Author(s):  
Julie A Ross ◽  
Kimberly J Johnson ◽  
James R Cerhan ◽  
Cindy K Blair ◽  
John T Soler ◽  
...  

Abstract Abstract 873 Each year in the United States, approximately 45,000 individuals are newly diagnosed with leukemia and 22,000 will die of the disease. Due to this poor survival, leukemia ranks fifth in person years of life lost among specific cancers. Little is known about causes, although exposure to solvents, radiation, pesticides and, to a modest extent, cigarette smoke has been implicated for some subtypes. The last comprehensive report of leukemia trends covered the period 1973–1998 [Xie Y et al, Cancer 2003]. Evaluation of recent leukemia incidence trends could provide important new etiologic insights. Using Surveillance, Epidemiology and End Results (SEER) Program data, we analyzed leukemia incidence trends in U.S. adults (≥ 20 years of age) by age, leukemia subtype (acute myeloid (AML), acute lymphoid (ALL), chronic myeloid (CML), chronic lymphoid (CLL)) sex, race, and ethnicity for the period 1987–2007. Frequencies, age-adjusted incidence rates (IR, per million), and trends were calculated along with annual percent change (APC) and corresponding 95% confidence intervals (CI). Joinpoint analyses were used to detect any significant directional changes in IRs over the period. Of 43,970 newly diagnosed cases identified, IRs increased with age and were consistently higher in males than females for all four subtypes. The highest IRs occurred for CLL (54.4), followed by AML (38.3), CML (20.6) and ALL (7.0). With regard to trends, IRs for CLL (APC -0.5; CI: -0.9, -0.1) and CML (APC -1.2; CI: -1.6, -0.8) declined over the time period; declines were observed in males and females, and by race and ethnicity. Male(M):Female(F) IR ratios remained relatively constant at approximately 2.0 and 1.7, respectively. For ALL, IRs decreased in males (APC -0.9; CI: -1.9, 0.2) but slightly increased in females (APC 0.4; CI: -1.0, 1.7), which was most notable in Hispanics (APC 4.0; CI: 1.2, 6.8). In contrast to CML and CLL, the overall M:F rate ratio for ALL decreased, although it did not reach statistical significance (p=0.08). For AML, IRs increased significantly for males (APC 1.0; CI: 0.3,1.6) and females (APC 1.7; CI: 0.7, 2.7) from 1987–2000 and 1987–2001, respectively. However, since then, AML IRs for males have been significantly decreasing by 4.2% per year (CI: -6.4, -2.1), while IRs for females have been decreasing by 1.6% per year (CI: -4.1, 0.9). Across the entire time period 1987–2007, there was a statistically significant negative trend (p=0.002) in the M:F IR ratio for AML. Decreasing IRs across many leukemias since 1987 are unlikely to reflect changes in screening or diagnostic coding practices. Instead, these observations may reflect temporal changes in etiologically relevant environmental exposures. Of note, the prevalence of cigarette smoking in the population has decreased and occupational safety practices (e.g., reducing solvent/radiation/pesticide exposure) have improved over the last several decades, which could contribute to the gradual decreases in some IRs observed. In contrast, the rapid and significant decrease noted for AML since 2000, especially following a significant increase, was striking and deserved additional scrutiny. We further consulted with our cancer registry colleagues to determine whether the introduction of myelodysplastic syndrome (MDS) as a new malignancy in SEER in 2001 could be influencing recent AML trends given the (apparently) coincidental overlap in time periods. Of note, approximately one third of MDS patients subsequently develop AML. We learned that AML following an MDS diagnosis from 2001–2009 was not reportable to SEER and therefore not counted. We are not aware that this has been documented in the literature. However, beginning for 2010 diagnoses, SEER changed this practice such that AML following MDS will be captured as a second malignancy. Based on these changes in AML surveillance, it will especially be important to monitor future trends for this malignancy. Overall, this study demonstrates the value of in-depth analyses of SEER cancer IRs and trends; analyses may reveal patterns of clinical and/or etiological importance, or, in the instance of AML, unpublished coding rule changes. Disclosures: No relevant conflicts of interest to declare.


2003 ◽  
Vol 7 (7) ◽  
Author(s):  
R Gilbert

The report on The Prevention and Control of Infections with Hepatitis Viruses in Correctional Settings (1) consolidates recommendations for the prevention and control of viral hepatitis in prisons. It was developed by the Centers for Disease Control and Prevention (CDC) in the United States (US) after consultation with federal agencies and specialists in the fields of corrections, correctional health care and public health at a meeting in Atlanta in March 2001.


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