Statistical approaches to the surveillance of infectious diseases for veterinary public health

This technical report covers the aspect of using statistical methodology for the monitoring of routinely collected surveillance data in veterinary public health. An account of the Farrington algorithm and Poisson cumulative sum schemes for the detection of aberrations is given with special attention devoted to the occurrence of seasonality and spatial aggregation of the time series. Modelling approaches for retrospective analysis of surveillance counts are described. To illustrate the applicability of the methodology in veterinary public health, data from the surveillance of rabies among fox in Hesse, Germany, are analysed

코로나19 비약물적 중재가 인플루엔자 발생에 미치는 영향: 시계열 예측을 중심으로

학위논문(석사) -- 서울대학교대학원 : 보건대학원 보건학과, 2023. 2. 조성일.Coronavirus disease-19(COVID-19) was first identified in Korea during the 2019-20 seasonal influenza epidemic. Social distancing measures, an effective non-pharmaceutical intervention, adopted to mitigate the spread of COVID-19 may have significant impact on influenza activity. This study aims to identify the changes in influenza activity during COVID-19 outbreak and assess the impact level of NPI intensity on influenza transmission. By comparing 2020-21 and 2021-22 seasonal influenza activity with 2013-19 seasons, it was found that COVID-19 outbreaks and associated NPIs such as use of face mask, school closure or travel restriction may have reduced the influenza incidence by 91%.The SARIMA(Seasonal Autoregressive Integrated Moving Average Model) were used to quantify the effectiveness of NPIs for the transmission of influenza virus. Without NPIs against COVID-19 during influenza epidemic season, ILI rate and positive rate of influenza virus would likely have remained high during the flu epidemic season, similar to those of previous seasons. This study identified the impact of NPI intensity on transmission of influenza, as the reduction rate increased when the social distancing level was strengthened (Step-by-step daily recovery: 58.10%, Special quarantine measures: 95.12%). These results suggest evidence for the role of NPIs and personal hygiene behavior in controlling influenza transmission in preparation for future outbreaks, and NPIs intervened against COVID-19 may be useful strategies for prevention and control of influenza epidemic.2019-2020 절기 인플루엔자 유행기간인 2020년 1월 20일 국내 코로나19 첫 확진자가 발생하였다. 지역사회 내 전파를 최소화하고자 사회적 거리두기, 개인위생 강화 등 여러 비약물적 중재가 시행되었으며 코로나19 외 인플루엔자를 포함한 여러 호흡기 감염병의 발생 양상에 큰 영향을 미친 것으로 알려졌다. 이에 본 연구에서는 코로나19 발생 이후 인플루엔자 발생 양상을 도출하고 인플루엔자 전파에서의 비약물적 중재의 효과를 확인하였다. 코로나19가 발생하기 이전인 2013-2019 절기와 2020-2022 절기의 인플루엔자 의사환자분율 및 인플루엔자 병원체 검출률을 비교한 결과 마스크 착용, 등교 중지, 출입국 제한 등 비약물적 중재가 시행된 시기에 인플루엔자 의사환자가 91% 감소한 것으로 나타났다. 사회적 거리두기의 단계가 인플루엔자 전파에 미치는 영향을 확인하기 위해 SARIMA 모델을 이용한 예측 모형을 구축하였다. 그 결과 코로나19의 비약물적 중재가 시행되지 않았을 경우 2020-2022 인플루엔자 유행은 전 절기와 비슷한 수준을 유지할 것으로 확인되었다. 또한, 사회적 거리두기 단계가 강화될 때 의사환자 감소율이 증가하는 것으로 나타났다 (단계적 일상회복 단계: 58.10%, 2021년 연말 특별 방역대책: 95.12%). 본 연구에서는 코로나19 시기에 강화된 개인위생과 비약물적 중재가 인플루엔자 발생 감소에 미치는 영향을 확인하였으며 이로써 본 연구 결과는 향후 인플루엔자 유행에 대비한 예방 및 관리 정책의 근거를 제시한다.Chapter 1. Introduction 1 1.1 Study Background 1 1.2 Literature Review 8 1.3 Purpose of Research 13 Chapter 2. Methods 14 2.1 Data Source 14 2.1 Descriptive Analysis 21 2.2. Time Series 22 2.3 Time Series Forecasting 27 Chapter 3. Results 32 3.1 Surveillance of IFV in Korea 32 3.2 Time Series Analysis 44 3.3 Time Series Forecasting 50 Chapter 4. Discussions 63 Chapter 5. Conclusion 74 Bibliography 75 Abstract in Korean 86 Appendix 88석

Enhancing the monitoring of fallen stock at different hierarchical administrative levels: an illustration on dairy cattle from regions with distinct husbandry, demographical and climate traits

Background: The automated collection of non-specific data from livestock, combined with techniques for data mining and time series analyses, facilitates the development of animal health syndromic surveillance (AHSyS). An example of AHSyS approach relates to the monitoring of bovine fallen stock. In order to enhance part of the machinery of a complete syndromic surveillance system, the present work developed a novel approach for modelling in near real time multiple mortality patterns at different hierarchical administrative levels. To illustrate its functionality, this system was applied to mortality data in dairy cattle collected across two Spanish regions with distinct demographical, husbandry, and climate conditions. Results: The process analyzed the patterns of weekly counts of fallen dairy cattle at different hierarchical administrative levels across two regions between Jan-2006 and Dec-2013 and predicted their respective expected counts between Jan-2014 and Jun- 2015. By comparing predicted to observed data, those counts of fallen dairy cattle that exceeded the upper limits of a conventional 95% predicted interval were identified as mortality peaks. This work proposes a dynamic system that combines hierarchical time series and autoregressive integrated moving average models (ARIMA). These ARIMA models also include trend and seasonality for describing profiles of weekly mortality and detecting aberrations at the region, province, and county levels (spatial aggregations). Software that fitted the model parameters was built using the R statistical packages. Conclusions: The work builds a novel tool to monitor fallen stock data for different geographical aggregations and can serve as a means of generating early warning signals of a health problem. This approach can be adapted to other types of animal health data that share similar hierarchical structures.info:eu-repo/semantics/publishedVersio

Detecting COVID-19 Outbreak with Anomalous Term Frequency

Previously many studies have aimed at predicting the trend of a disease through time series forecasting using machine learning methods. However, data extracted from the real world is often noisy, which can pose numerous challenges for directly predicting the trend, and therefore leading to suboptimal prediction results. Furthermore, real-world data is usually very large, that is, having very long time periods. When it comes to data of such scale, trend forecasting becomes intractable even to state-of-the-art forecasting algorithms such as RNN-LSTM. In the past, not much research has been conducted in applying anomaly detection for disease outbreak detection, including the most recent COVID-19 pandemic. Consequently, in this research, we propose redefining the problem into outbreak detection, which aims to predict whether a future point is or is not a sign of a large scaled COVID-19 outbreak. Through simplifying a complex regression problem into a binary classification problem, the requirements of the learning model may be decreased and therefore the learning performance may be enhanced