The changing epidemiology of hemorrhagic fever with renal syndrome in Southeastern China during 1963–2020: A retrospective analysis of surveillance data

Background Hemorrhagic fever with renal syndrome (HFRS) is a rodent-borne disease caused by hantavirus which was endemic Zhejiang Province, China. In this study, we aim to explore the changing epidemiology of HFRS in Zhejiang, identify high-risk areas and populations, and evaluate relevant policies and interventions to better improve HFRS control and prevention. Methods Surveillance data on HFRS during 1963–2020 in Zhejiang Province were extracted from Zhejiang Provincial Center for Disease Control and Prevention archives and the Chinese Notifiable Disease Reporting System. The changing epidemiological characteristics of HFRS including seasonal distribution, geographical distribution, and demographic features, were analyzed using joinpoint regression, autoregressive integrated moving average model, descriptive statistical methods, and Spatio-temporal cluster analysis. Results From 1963 to 2020, 114 071 HFRS cases and 1269 deaths were reported in Zhejiang Province. The incidence increased sharply from 1973 and peaked in 1986, then decreased steadily and maintained a stable incidence from 2004. HFRS cases were reported in all 11 prefecture-level cities of Zhejiang Province from 1963 to 2020. The joint region (Shengzhou, Xinchang, Tiantai, and surrounding areas), and Kaihua County are the most seriously affected regions throughout time. After 1990, the first HFRS incidence peak was in May-June, with another one from November to January. Most HFRS cases occurred in 21- (26.48%) and 30- years group (24.25%) from 1991 to 2004, but 41- (25.75%) and 51-years (23.30%) had the highest proportion from 2005 to 2020. Farmers accounted for most cases (78.10%), and cases are predominantly males with a male-to-female ratio of 2.6:1. It was found that the median time from onset to diagnosis was 6.5 days (IQR 3.75–10.42), and the time from diagnosis to disease report was significantly shortened after 2011. Conclusions We observed dynamic changes in the seasonal distribution, geographical distribution, and demographic features of HFRS, which should be well considered in the development of control and prevention strategies in future. Additional researches are warranted to elucidate the environmental, meteorological, and social factors associated with HFRS incidence in different decades.

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SARFIMA model prediction for infectious diseases: application to hemorrhagic fever with renal syndrome and comparing with SARIMA

10.21203/rs.2.22527/v3 ◽

2020 ◽

Author(s):

Chang Qi ◽

Dandan Zhang ◽

Yuchen Zhu ◽

Lili Liu ◽

Chunyu Li ◽

...

Keyword(s):

Infectious Diseases ◽

Goodness Of Fit ◽

Moving Average ◽

Predictive Ability ◽

Information Criterion ◽

Hemorrhagic Fever ◽

Forecast Performance ◽

Sarima Model ◽

Control And Prevention ◽

And Control

Abstract Background The early warning model of infectious diseases plays a key role in prevention and control. Our study aims to using seasonal autoregressive fractionally integrated moving average (SARFIMA) model to predict the incidence of hemorrhagic fever with renal syndrome (HFRS) and comparing with seasonal autoregressive integrated moving average (SARIMA) model to evaluate its prediction effect. Methods Data on notified HFRS cases in Weifang city, Shandong Province were collected from the Disease Reporting Information System of the Shandong Center for Disease Control and Prevention between January 1, 2005 and December 31, 2018. The SARFIMA model considering both the short memory and long memory was performed to fit and predict the HFRS series. Besides, we compared accuracy of fit and prediction between SARFIMA and SARIMA which was used widely in infectious diseases. Results Model assessments indicated that the SARFIMA model has better goodness of fit (SARFIMA(1, 0.11, 2)(1, 0, 1) 12 : Akaike information criterion (AIC): -631.31; SARIMA(1, 0, 2)(1, 1, 1) 12 : AIC: -227.32) and better predictive ability than the SARIMA model (SARFIMA: root mean square error (RMSE): 0.058; SARIMA: RMSE: 0.090). Conclusions The SARFIMA model produces superior forecast performance than the SARIMA model for HFRS. Hence, the SARFIMA model may help to improve the forecast of monthly HFRS incidence based on a long-range dataset.

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SARFIMA model prediction for infectious diseases: application to hemorrhagic fever with renal syndrome and comparing with SARIMA

BMC Medical Research Methodology ◽

10.1186/s12874-020-01130-8 ◽

2020 ◽

Vol 20 (1) ◽

Author(s):

Chang Qi ◽

Dandan Zhang ◽

Yuchen Zhu ◽

Lili Liu ◽

Chunyu Li ◽

...

Keyword(s):

Infectious Diseases ◽

Goodness Of Fit ◽

Moving Average ◽

Predictive Ability ◽

Information Criterion ◽

Hemorrhagic Fever ◽

Forecast Performance ◽

Sarima Model ◽

Control And Prevention ◽

And Control

Abstract Background The early warning model of infectious diseases plays a key role in prevention and control. This study aims to using seasonal autoregressive fractionally integrated moving average (SARFIMA) model to predict the incidence of hemorrhagic fever with renal syndrome (HFRS) and comparing with seasonal autoregressive integrated moving average (SARIMA) model to evaluate its prediction effect. Methods Data on notified HFRS cases in Weifang city, Shandong Province were collected from the official website and Shandong Center for Disease Control and Prevention between January 1, 2005 and December 31, 2018. The SARFIMA model considering both the short memory and long memory was performed to fit and predict the HFRS series. Besides, we compared accuracy of fit and prediction between SARFIMA and SARIMA which was used widely in infectious diseases. Results Model assessments indicated that the SARFIMA model has better goodness of fit (SARFIMA (1, 0.11, 2)(1, 0, 1)12: Akaike information criterion (AIC):-631.31; SARIMA (1, 0, 2)(1, 1, 1)12: AIC: − 227.32) and better predictive ability than the SARIMA model (SARFIMA: root mean square error (RMSE):0.058; SARIMA: RMSE: 0.090). Conclusions The SARFIMA model produces superior forecast performance than the SARIMA model for HFRS. Hence, the SARFIMA model may help to improve the forecast of monthly HFRS incidence based on a long-range dataset.

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The Epidemiological Characteristics of Deaths with COVID-19 in The Early Stage in Wuhan, China

10.21203/rs.3.rs-93945/v1 ◽

2020 ◽

Author(s):

Jianjun Bai ◽

Fang Shi ◽

Jinhong Cao ◽

Haoyu Wen ◽

Fang Wang ◽

...

Keyword(s):

Geographical Distribution ◽

Symptom Onset ◽

Early Stage ◽

Central Area ◽

Control Measures ◽

Age Group ◽

Female Ratio ◽

Underlying Diseases ◽

Epidemiological Characteristics ◽

Median Interval

Abstract Objectives To analyze the epidemiological characteristics of deaths of COVID-19 in Wuhan, China and understand the changing trends of the COVID-19 epidemic and the effects of prevention and control measures in Wuhan.Methods Through the China's Infectious Disease Information System, we collected deaths’ information in Wuhan. We analyzed the patient's demographic characteristics, drew epidemiological curve, made distribution map of epidemic situation, etc. @Risk for fitting distribution, SPSS for statistical analysis, and ArcGIS for mapping.Results As of February 24, 2020, a total of 1833 unique deaths were extracted. Among the deaths with COVID-19, the mild type accounted for the most, 37.2%, followed by severe type, 30.1%. The median age was 70.0 (inter quartile range: 63.0-79.0) years, most of the deaths were distributed in 50-89 age group; no deaths occurred in 0-9 age group; and the male to female ratio was 1.95. A total of 65.7% of the deaths in Wuhan combined with underlying diseases, and the deaths with underlying diseases were mainly male; the main combined underlying diseases were hypertension, diabetes and cardiovascular diseases. The peak of daily deaths appeared on February 14 and then declined after February 14. The median interval from symptom onset to diagnosis was 10.0 (6.0-14.0) days; the interval from onset to diagnosis gradually shortened. The median interval from diagnosis to death was 6.0 (2.0-11.0)days; The median interval from symptom onset to deaths was 17.0 (12.0-22.0)days, respectively. In terms of geographical distribution, the central urban area was more serious. Wuchang District had the highest number of deaths, and Jianghan District had the highest death rate.Conclusion COVID-19 posed a greater threat to the elderly and more men than women, especially elderly men with underlying diseases. The geographical distribution showed that the epidemic in the central area of Wuhan is more serious than in the surrounding areas. Analysis of deaths as of February 24 indicates that the COVID-19 epidemic in Wuhan has achieved a tremendous improvement, and the strong epidemic control measures taken by Wuhan Government were very effective.

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SARFIMA model prediction for infectious diseases: application to hemorrhagic fever with renal syndrome and comparing with SARIMA

10.21203/rs.2.22527/v1 ◽

2020 ◽

Author(s):

Chang Qi ◽

Dandan Zhang ◽

Yuchen Zhu ◽

Lili Liu ◽

Chunyu Li ◽

...

Keyword(s):

Infectious Diseases ◽

Goodness Of Fit ◽

Moving Average ◽

Predictive Ability ◽

Information Criterion ◽

Hemorrhagic Fever ◽

Forecast Performance ◽

Sarima Model ◽

Control And Prevention ◽

And Control

Abstract Background The early warning model of infectious diseases plays a key role in prevention and control. Our study aims to using seasonal autoregressive fractionally integrated moving average (SARFIMA) model to predict the incidence of hemorrhagic fever with renal syndrome (HFRS) and comparing with seasonal autoregressive integrated moving average (SARIMA) model to evaluate its prediction effect. Methods Data on notified HFRS cases in Weifang city, Shandong Province were supplied by the Disease Reporting Information System of the Shandong Center for Disease Control and Prevention from January 1, 2005 to December 31, 2018. The SARFIMA model considering both the short-memory and long-memory were performed to fit and predict the HFRS series. Besides, we compared accuracy of fitting and prediction between SARFIMA and SARIMA which were used widely in infectious diseases. Results Both SARFIMA and SARIMA models show good fit of data. Model assessments indicated that the SARFIMA model has better goodness of fit (SARFIMA(2, 0.15, 2)(1, 0, 0) 12 : Akaike information criterion (AIC): -630.61; SARIMA(2, 0, 2)(1, 1, 0) 12 : AIC: -196.04) and better predictive ability than the SARIMA model (SARFIMA: root mean square error (RMSE): 0.067; SARIMA: RMSE: 0.111). Conclusions The SARFIMA model produces superior forecast performance than the SARIMA model for HFRS. Hence, the SARFIMA model may help us to improve the forecast of HFRS incidence.

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SARFIMA model prediction for infectious diseases: application to hemorrhagic fever with renal syndrome and comparing with SARIMA

10.21203/rs.2.22527/v2 ◽

2020 ◽

Author(s):

Chang Qi ◽

Dandan Zhang ◽

Yuchen Zhu ◽

Lili Liu ◽

Chunyu Li ◽

...

Keyword(s):

Infectious Diseases ◽

Goodness Of Fit ◽

Moving Average ◽

Predictive Ability ◽

Information Criterion ◽

Hemorrhagic Fever ◽

Forecast Performance ◽

Sarima Model ◽

Control And Prevention ◽

And Control

Abstract Background: The early warning model of infectious diseases plays a key role in prevention and control. Our study aims to using seasonal autoregressive fractionally integrated moving average (SARFIMA) model to predict the incidence of hemorrhagic fever with renal syndrome (HFRS) and comparing with seasonal autoregressive integrated moving average (SARIMA) model to evaluate its prediction effect. Methods: Data on notified HFRS cases in Weifang city, Shandong Province were collected from the Disease Reporting Information System of the Shandong Center for Disease Control and Prevention between January 1, 2005 and December 31, 2018. The SARFIMA model considering both the short memory and long memory was performed to fit and predict the HFRS series. Besides, we compared accuracy of fit and prediction between SARFIMA and SARIMA which was used widely in infectious diseases.Results: Model assessments indicated that the SARFIMA model has better goodness of fit (SARFIMA(1, 0.11, 2)(1, 0, 1)12: Akaike information criterion (AIC):-631.31; SARIMA(1, 0, 2)(1, 1, 1)12: AIC: -227.32) and better predictive ability than the SARIMA model (SARFIMA: root mean square error (RMSE):0.058; SARIMA: RMSE: 0.090).Conclusions: The SARFIMA model produces superior forecast performance than the SARIMA model for HFRS. Hence, the SARFIMA model may help to improve the forecast of monthly HFRS incidence based on a long-range dataset.

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LINC-28. EPIDEMIOLOGICAL CHARACTERISTICS AND SURVIVAL OUTCOMES OF CHILDREN WITH MEDULLOBLASTOMA TREATED AT THE NATIONAL CANCER INSTITUTE (INCA) IN RIO DE JANEIRO, BRAZIL

Neuro-Oncology ◽

10.1093/neuonc/noaa222.462 ◽

2020 ◽

Vol 22 (Supplement_3) ◽

pp. iii383-iii384

Author(s):

Gabriela Oigman ◽

Diana Osorio ◽

Joseph Stanek ◽

Jonathan Finlay ◽

Denizar Vianna ◽

...

Keyword(s):

High Risk ◽

Maternal Education ◽

Survival Outcomes ◽

Middle Income ◽

Female Ratio ◽

High Risk Patients ◽

Presenting Symptoms ◽

Risk Patients ◽

Epidemiological Characteristics ◽

Standard Risk

Abstract BACKGROUND Medulloblastoma (MB), the most malignant brain tumor of childhood has survival outcomes exceeding 80% for standard risk and 60% for high risk patients in high-income countries (HIC). These results have not been replicated in low-to-middle income countries (LMIC), where 80% of children with cancer live. Brazil is an upper-middle income country according to World Bank, with features of LMIC and HIC. METHODS We conducted a retrospective review of 126 children (0–18 years) diagnosed with MB from 1997 to 2016 at INCA. Data on patients, disease characteristics and treatment information were retrieved from the charts and summarized descriptively; overall survival (OS) and event-free survival (EFS) were calculated using the Kaplan-Meier Method. RESULTS The male/female ratio was 1.42 and the median age at diagnosis was 7.9 years. Headache (79%) and nausea/vomiting (75%) were the most common presenting symptoms. The median time from onset of symptoms to surgery was 50 days. The OS for standard-risk patients was 69% and 53% for high-risk patients. Patients initiating radiation therapy within 42 days after surgery (70.6% versus 59.6% p=0.016) experienced better OS. Forty-five patients (35%) had metastatic disease at admission. Lower maternal education correlated with lower OS (71.3% versus 49% p=0.025). Patients who lived >40km from INCA fared better (OS= 68.2% versus 51.1% p=0.032). Almost 20% of families lived below the Brazilian minimum wage. CONCLUSIONS These findings suggest that socioeconomic factors, education, early diagnosis and continuous data collection, besides oncological treatment must be adressed to improve the survival of children with MB.

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Epidemiological profile of Plasmodium ovale spp. imported from Africa to Anhui Province, China, 2012–2019

Malaria Journal ◽

10.1186/s12936-020-03551-8 ◽

2021 ◽

Vol 20 (1) ◽

Author(s):

Tao Zhang ◽

Shuqi Wang ◽

Duoquan Wang ◽

Sarah Auburn ◽

Shenning Lu ◽

...

Keyword(s):

Information System ◽

Disease Control ◽

Latency Period ◽

Anhui Province ◽

Detection Methods ◽

Health Concern ◽

Plasmodium Ovale ◽

Control And Prevention ◽

Endemic Areas ◽

Epidemiological Characteristics

Abstract Background Although autochthonous malaria cases are no longer reported in Anhui Province, China, imported malaria has become a major health concern. The proportion of reported malaria cases caused by Plasmodium ovale spp. increased to levels higher than expected during 2012 to 2019, and showed two peaks, 19.69% in 2015 and 19.35% in 2018. Methods A case-based retrospective study was performed using data collected from the China Information System for Disease Control and Prevention (CISDCP) and Information System for Parasitic Disease Control and Prevention (ISPDCP) from 2012 to 2019 to assess the trends and differences between Plasmodium ovale curtisi (P. o. curtisi) and Plasmodium ovale wallikeri (P. o. wallikeri). Epidemiological characteristics were analyzed using descriptive statistics. Results Plasmodium o. curtisi and P. o. wallikeri were found to simultaneously circulate in 14 African countries. Among 128 patients infected with P. ovale spp., the proportion of co-infection cases was 10.16%. Six cases of co-infection with P. ovale spp. and P. falciparum were noted, each presenting with two clinical attacks (the first attack was due to P. falciparum and the second was due to P. ovale spp.) at different intervals. Accurate identification of the infecting species was achieved among only 20.00% of cases of P. ovale spp. infection. At the reporting units, 32.17% and 6.96% of cases of P. ovale spp. infection were misdiagnosed as P. vivax and P. falciparum infections, respectively. Conclusion The present results indicate that the potential of P. ovale spp. to co-infect with other Plasmodium species has been previously underestimated, as is the incidence of P. ovale spp. in countries where malaria is endemic. P. o. curtisi may have a long latency period of > 3 years and potentially cause residual foci, thus posing challenges to the elimination of malaria in P. ovale spp.-endemic areas. Considering the low rate of species identification, more sensitive point-of-care detection methods need to be developed for P. ovale spp. and introduced in non-endemic areas.

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Predicting the number of cases of dengue infection in Ribeirão Preto, São Paulo State, Brazil, using a SARIMA model

Cadernos de Saúde Pública ◽

10.1590/s0102-311x2011000900014 ◽

2011 ◽

Vol 27 (9) ◽

pp. 1809-1818 ◽

Cited By ~ 12

Author(s):

Edson Zangiacomi Martinez ◽

Elisângela Aparecida Soares da Silva

Keyword(s):

Moving Average ◽

Dengue Infection ◽

Arima Model ◽

Sao Paulo ◽

São Paulo ◽

Incidence Data ◽

Paulo State ◽

Control And Prevention ◽

São Paulo State ◽

Predicted Values

This study aimed to develop a forecasting model for the incidence of dengue in Ribeirão Preto, São Paulo State, Brazil, using time series analysis. The model was performed using the Seasonal Autoregressive Integrated Moving Average (SARIMA). Firstly, we fitted a model considering monthly notifications of cases of dengue recorded from 2000 to 2008 in Ribeirão Preto. We then extracted predicted values for 2009 from the adjusted model and compared them with the number of cases observed for that year. The SARIMA (2,1,3)(1,1,1)12 model offered best fit for the dengue incidence data. The results showed that the seasonal ARIMA model predicts the number of dengue cases very effectively and reliably, and is a useful tool for disease control and prevention.

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Paediatric visceral leishmaniasis: a retrospective study on clinical manifestations, demographic features and laboratory findings of hospitalised cases in Iran between 2006 and 2016

Tropical Doctor ◽

10.1177/0049475518811513 ◽

2018 ◽

Vol 49 (1) ◽

pp. 59-61 ◽

Cited By ~ 1

Author(s):

Manijeh Nourian ◽

Aliehsan Heidari ◽

Saleheh Tajali ◽

Erfan Ghasemi ◽

Mehdi Mohebali ◽

...

Keyword(s):

Retrospective Study ◽

Visceral Leishmaniasis ◽

Clinical Manifestations ◽

Laboratory Findings ◽

Neglected Disease ◽

Paediatric Hospital ◽

Geographical Regions ◽

Epidemiological Characteristics ◽

Demographic Features

Visceral leishmaniasis (VL) is a neglected disease. Our retrospective study describes 38 clinical and epidemiological characteristics of VL in patients admitted to a paediatric hospital in Tehran, Iran, who came from different geographical regions, indicating that the disease has spread to most parts of the country. Some 76.3% of the children documented suffered with symptoms of the disease for two months before admission.

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