Moving Average Model Research Articles

Financial time series forecasting methods

The paper presents the development of time series forecasting algorithms based on the Integrated Autoregressive Moving Average Model (ARIMA) and the Fourier Expansion model. These models were applied to non-stationary time series of stock quotes after bringing these series to a stationary form. In the paper, ARIMA and Fourier Expansion model were constructed, using Python development environment. The developed algorithms were tested on Russian and American stock indices using the Mean Absolute Percentage Error metric.

Modeling and Forecasting of Consumer Price Index of Foods and Non-Alcoholic Beverages in Kenya Using Autoregressive Integrated Moving Average Models

Modeling and Forecasting of Consumer Price Index of Foods and Non-Alcoholic Beverages in Kenya Using Autoregressive Integrated Moving Average Models

A Hybrid GARCH and Deep Learning Method for Volatility Prediction

Volatility prediction plays a vital role in financial data. The time series movements of stock prices are commonly characterized as highly nonlinear and volatile. This study is aimed at enhancing the accuracy of return volatility forecasts for stock prices by investigating the prediction of their price volatility through the integration of diverse models. Thus, the study integrated four powerful methods: seasonal autoregressive (AR) integrated moving average (MA), generalized AR conditional heteroskedasticity (ARCH) family models, convolutional neural network (CNN), and bidirectional long short‐term memory (LSTM) network. The hybrid model was developed using the residuals generated by the seasonal AR integrated MA model as input for the generalized ARCH model. Following this, the estimated volatility obtained was utilized as an input feature for both the hybrid CNNs and bidirectional LSTM models. The model’s forecasting performance was assessed using key evaluation metrics, including mean absolute error (MAE) and root mean squared error (RMSE). Compared to other hybrid models, our new proposed hybrid model demonstrates an average reduction in MAE and RMSE of 60.35% and 60.61%, respectively. The experimental results show that the model proposed in this study has good performance and accuracy in predicting the volatility of stock prices. These findings offer valuable insights for financial data analysis and risk management strategies.

Speech recognition enhancement based on wireless network sensors application in interactive intelligent teaching system

The speech function used in the multi-dimensional speech recognition model based on MTL and RNN is the commonly used MFCC function, and in feature extraction, after various filtering and conversion, deleting part of the speech information is a requirement for 3D audio recognition. Use multidimensional information as much as possible. Based on this, this article not only brings the development of convolutional neural network intelligent teaching, but also proposes a new education model. Heuristic education like flipped classroom is still the content of education research in recent years, and it is important to the current education form in China. It is mainly based on the traditional lecture mode or the classroom form. This article focuses on undergraduate education. It is designed in the classroom based on the video data of the undergraduate interactive activities and the interactive system. It has important research significance and practicality. At the same time, the interactive content of the traditional teaching method is used to perform voice recognition, and the recognition effect of the main body of the interactive system is further improved through the voice information. The spatial perspective is used to divide the lens perspective into seminars and experimental forms, and the pixel change value of each frame provides a specific reference for it to determine the reflection of student interaction activities and the evaluation of student seminars. This article introduces the implementation process of interactive content and interactive activities of the teaching system in the classroom, introduces the speech recognition framework of the MARS Chinese teaching system, uses the model principle, and compares the general speech data set test with the actual ARST model audio. The results show that, MAS model performance is more advantageous.

Towards Integration of Religious Values and Transformational Leadership to Improve Cooperatives Performance

This study aims to explore a new empirical model of Mas'uliyah Transformational Leadership (MTL) formed through organizational learning (OL) and Islamic change motivation (ICM) to increase member participation and organizational performance. MTL is a new concept that is an integration between transformational leadership and religious values. Data were collected from 223 cooperative management respondents in Central Java, Indonesia, using a questionnaire. Furthermore, data analysis was carried out using the SEM method. The results of the research show that the new MTL concept developed in this study can be leveraged from the Islamicchange motivation possessed by the leaders. Furthermore, leaders with the MTL style can increase the participation of cooperative members, and ultimately improve overall performance.

Stock Price Undulation Prediction Using ARIMA Model

Stock is one of the most important investments nowadays, but it is also changing very rapidly. Its dramatic changes may lead to property damage. The stock price can influence the market situation. If its future price can be predicted, peoples investments will be more accurate. To solve the problem quickly, creating models seems to be the best choice, While the influences keep changing, some easy models cannot work well. By comparison between many models, we decided to choose Auto-regressive Integrated Moving Average Model (ARIMA). ARIMA model is a set of AR model and MA model. ARIMA model uses historical information of the data to predict the future. Though ARIMA model cannot respond well to emergencies. Assume that the average month price of the stock is steady. This paper uses this model to predict whether the investor will gain or loss. Through SPSS, the model is built and tested. The models correct rate is higher than 0.7, so it can greatly help people to make decisions.

Problem and Prospect of Pharmaceuticals Industry of Bangladesh amid LDC Graduation

Aims: The paper aims to identify the prospects and challenges of the pharmaceutical sector of Bangladesh after graduation and explores potential areas of market and product diversification. Addressing the upcoming graduation challenges, the paper sheds light on trade-related challenges of pharmaceutical industry and digs out a way forward. Study Design: The study is conducted using primary data from the Key Informant Interview (KII) and secondary data from different publications and sources. A qualitative survey tool is used for assessing the view of stakeholders and graduation challenges for pharmaceutical sector. Methodology: The study uses qualitative tools to evaluate the area of market and product diversification of the pharmaceutical industry and finally uses the Autoregressive Integrated Moving Average (ARIMA) model to forecast export figure from 2023 to 2032 and what interventions need to be taken to reach the expected level of growth amid LDC graduation. Results: The pharmaceutical industry will confront significant hurdles in terms of value addition, TRIPS-waiver erosion, patent payments, and license fees for producing pharmaceuticals product and medicine after graduation. Moreover, the industry faces difficulties due to excessive reliance on Active Pharmaceutical Ingredient (API) imports, limited diversified products, lack of Research and Development (R&D) and lab testing facilities, lack of infrastructure in the API park, dearth of bioequivalence test facilities, and patentable molecules in the post-graduation era. Recommendation: Bangladesh needs to accentuate API production, R&D, reverse engineering, and technical know-how as well as make a functional API Park, develop a curriculum to have experts in synthesis chemistry, and establish an accredited bioequivalence study center to secure patentable molecules and API.

A study of the transaction volume prediction problem based on recurrent neural networks

With the rapid development of artificial intelligence technology, intelligent fintech scenarios based on big data are receiving more and more attention, and through the analysis of massive financial class data, accurate decision support can be provided for its various scenarios. By predicting the transaction volume of a financial product of a bank, abnormal transaction flow and gradual change trend can be found 1 day in advance to provide decision support for business department program development, and provide decision support for system expansion and contraction, thus reducing system online pressure or releasing unnecessary system resources. Linear algorithms such as AR model, MA model, ARMA model, etc. have poor prediction results for transaction volumes during holidays in the non-stationary dataset handled in this study due to strong assumptions on historical data. In this paper, we design and implement an LSTM-based trading volume prediction model LSTM-WP (LSTM-WebPredict) using deep learning algorithm, which can improve the accuracy of prediction of holiday trading volume by about 8% based on the linear algorithm by discovering and learning the features of historical data, and the learning ability of the model will gradually increase with the increasing of training data; Not only that, the research of this algorithm also provides corresponding technical accumulation for other business scenarios of time series problems, such as trend prediction and capacity assessment.

Stochastic Time Series Analysis, Modeling, and Forecasting of Weekly Rainfall Using Sarima Model

Rainfall holds critical significance for water resource applications, particularly in rainfed agricultural systems. This study employs the Autoregressive Integrated Moving Average (ARIMA) technique, a data mining approach commonly used for time series analysis and future forecasting. Given the increasing importance of climate change forecasting in averting unexpected natural hazards such as floods, frost, forest fires, and droughts, accurate weather data forecasting becomes imperative. The objective of this study was to develop a Seasonal Auto-Regressive Integrative Moving Average (SARIMA) model for forecasting weekly rainfall in Junagadh Station, Gujarat. Utilizing 53 years of historical data (1963 to 2016), the SARIMA model predicts weekly rainfall for the subsequent five years (2018 to 2022). Through comprehensive evaluation using ACF and PACF plots, AIC, SBC, MAPE, and MAE values, the study identifies SARIMA (0,0,4)(0,1,1)52 as the optimal model, offering the most accurate prediction. The robust results affirm that the SARIMA model provides reliable and satisfactory weekly rainfall predictions. This research contributes valuable insights into the precision and efficacy of SARIMA models for rainfall forecasting, aiding in strategic water resource management in the Junagadh region.

Aeroelastic System Identification for Flutter Prediction via Multi-Output Autoregressive Modeling

The paper presents a study of aeroelastic system identification and flutter prediction in a wind tunnel test. Multi-output autoregressive moving-average and multi-output autoregressive models are fitted to acceleration and strain data and their correlation functions, respectively, to generate models that account for the dependencies between the different measured data. The modal frequencies and damping values are extracted from these models and used to assess the flutter margin at different airspeeds and predict the flutter onset speed. The method is applied in a flutter prediction wind tunnel study of a full-scale, half-span aeroelastic demonstrator aircraft using multiple acceleration data and strain data from fiber-optic strain sensors. Both models provide a smooth variation of the modal parameters (frequencies and damping values), from which it is easy to identify the critical aeroelastic modes that coalesce in flutter. Based on the different data sets, all models accurately predict flutter onset at approximately the same airspeed. An advantage of the proposed system identification approaches is that they concurrently account for the data from multiple sensors, avoiding the need to average the modal dynamics estimated from different sensors. The proposed method can be used to support safe flutter tests, as accurate system identification leads to accurate flutter prediction based on structural data recorded at safe airspeeds, remote from flutter.

Pandemic Effect on Healthcare Use and Death in Adults with Epilepsy: A Population Study.

We conducted a population-based study using Ontario health administrative data to describe trends in healthcare utilization and mortality in adults with epilepsy during the first pandemic year (March 2020-March 2021) compared to historical data (2016-2019). We also investigated if changes in outpatient visits and diagnostic testing during the first pandemic year were associated with increased risk for hospitalizations, emergency department (ED) visits, or death. Projected monthly visit rates (per 100,000 people) for outpatient visits, electroencephalography, magnetic resonance, computed tomography, all-cause ED visits, hospitalizations, and mortality were calculated based on historical data by fitting monthly time series autoregressive integrated moving-average models. Two-way interactions were calculated using Quasi-Poisson models. In adults with epilepsy during the first quarter of the pandemic, we demonstrated a reduction in all-cause outpatient visits, diagnostic testing, ED visits and hospitalizations, and a temporary increase in mortality (observed rates of 355.8 vs projected 308.8, 95% CI: 276.3-345.1). By the end of the year, outpatient visits increased (85,535.4 vs 76,620.6, 95% CI: 71,546.9-82,059.4), and most of the diagnostic test rates returned to the projected. The increase in the rate of all-cause mortality during the pandemic, compared to pre-pandemic, was greater during months with the lower frequency of diagnostic tests than months with higher frequency (interaction p-values <.0001). We described the impact of the pandemic on healthcare utilization and mortality in adults with epilepsy during the first year. We demonstrated that access to relevant diagnostic testing is likely important for this population while planning restrictions on non-urgent health services.

Modelling Infant Mortality Rate using Time Series Models

The world’s main indicator of children’s health and general development is the infant mortality rate for infant under the age of five. Infant mortality is the term used to describe the death of a child before their first birthday. The infant mortality rate (IMR), which is the number of deaths of infants under one year of age per 1,000 live births, can be used to describe the prevalence of infant mortality in a population. Comparing the death rate of children under the age of five is the child mortality rate, commonly referred to as the under-five mortality rate. Nigeria, one of the nations with a high under-five mortality rate of 117 per 1,000 live births in 2019, is among those nations. The nation is among the top five nations with the highest mortality rate for children under five in 2019. This study aims to model infant mortality(Live birth and Still birth) rate using time series models and to predict the mortality rate using these models. Adeoyo Maternity Hospital Yemetu in Ibadan provided the data for this study. The data set is a monthly data and also a secondary data span for a period of 12 years (2009 to 2020). The time plot showed visual inspection and non-stationarity. Differencing was done and the unit root test performed for the purpose of comparison thereafter. Augmented-Dickey Fuller test and Phillip Perron unit root test was further tested for the establishment of stationarity in order to the main objectives. Three time series methods are the Autoregressive Integrated Moving Average Model(ARIMA), Exponential Smoothing and the Holt-Winters Method were used to model and predict the infant mortality rate data. The result shows that ARIMA order=c(0,0,1) with zero (0) mean for stillbirth and ARIMA order=c(1,0, 2) for live birth with the smallest AIC = (9.102 and 13.991). Akaike Information Criterion(AIC) values of (9.289, 14.139) and (9.102, 13.991) for live birth and still birth, respectively, were derived by exponential smoothing and Holtwinters technique. This means that Holtwinters' technique, which yielded the lowest AIC when compared to ARIMA and exponential smoothing, is the most accurate predictor of both stillbirth and live birth data. Given the high mortality rate for children under the age of five, it is crucial for the government to place more of an emphasis on health issues and to solve the problems plaguing Nigeria's child health care system.

Impacts of crude oil market on global economy: Evidence from the Ukraine-Russia conflict via fuzzy models

The increasing Russia-Ukraine crisis is without a doubt Europe's most prominent conflict since World War II, changing the dynamics of the oil and other key markets. Because the oil market has traditionally interacted with other financial and commodity markets, it will be intriguing to examine how it interacts with substantial financial assets amid market volatility induced by a conflict. The goal of this study is to propose a fuzzy time series (FTS) model and to compare its competitiveness to existing fuzzy time series (FTS) models, Autoregressive Integrated Moving Average (ARIMA) model and some machine learning methods i.e. Artificial Neural Networks (ANN), Support Vector Machine (SVM) and XGBoost models. We considered changes in the partitioning universe of discourse, optimization of parameters method(s), and interval estimation to make the forecast accuracy more precise forecasting than traditional methods via MAPE. The event-based data results show the proposed fuzzy time series model is outperforming all the competitive methods in the study. Furthermore, the proposed model forecasting shows a future decline tendency in WTi market crude oil prices (US$/BBL) after being at the record highest level, which is good news for the worldwide economy.

Trends in outpatient and inpatient visits for separate ambulatory-care-sensitive conditions during the first year of the COVID-19 pandemic: a province-based study.

The COVID-19 pandemic led to global disruptions in non-urgent health services, affecting health outcomes of individuals with ambulatory-care-sensitive conditions (ACSCs). We conducted a province-based study using Ontario health administrative data (Canada) to determine trends in outpatient visits and hospitalization rates (per 100,000 people) in the general adult population for seven ACSCs during the first pandemic year (March 2020-March 2021) compared to previous years (2016-2019), and how disruption in outpatient visits related to acute care use. ACSCs considered were chronic obstructive pulmonary disease (COPD), asthma, angina, congestive heart failure (CHF), hypertension, diabetes, and epilepsy. We used time series auto-regressive integrated moving-average models to compare observed versus projected rates. Following an initial reduction (March-May 2020) in all types of visits, primary care outpatient visits (combined in-person and virtual) returned to pre-pandemic levels for asthma, angina, hypertension, and diabetes, remained below pre-pandemic levels for COPD, and rose above pre-pandemic levels for CHF (104.8 vs. 96.4, 95% CI: 89.4-104.0) and epilepsy (29.6 vs. 24.7, 95% CI: 22.1-27.5) by the end of the first pandemic year. Specialty visits returned to pre-pandemic levels for COPD, angina, CHF, hypertension, and diabetes, but remained above pre-pandemic levels for asthma (95.4 vs. 79.5, 95% CI: 70.7-89.5) and epilepsy (53.3 vs. 45.6, 95% CI: 41.2-50.5), by the end of the year. Virtual visit rates increased for all ACSCs. Among ACSCs, reductions in hospitalizations were most pronounced for COPD and asthma. CHF-related hospitalizations also decreased, albeit to a lesser extent. For angina, hypertension, diabetes, and epilepsy, hospitalization rates reduced initially, but returned to pre-pandemic levels by the end of the year. This study demonstrated variation in outpatient visit trends for different ACSCs in the first pandemic year. No outpatient visit trends resulted in increased hospitalizations for any ACSC; however, reductions in rates of asthma, COPD, and CHF hospitalizations persisted.

Initiation of opioid agonist therapy after hospital visits for opioid poisonings in Ontario.

Emergency department visits and hospital admissions for opioid toxicity are opportunities to initiate opioid agonist therapy (OAT), which reduces morbidity and mortality in patients with opioid use disorder (OUD). The study objectives were to evaluate OAT initiation rates after a hospital encounter for opioid toxicity in Ontario, Canada, and determine whether publication of a 2018 Canadian OUD management guideline was associated with increased initiation. We conducted a retrospective, population-based serial cross-sectional study of hospital encounters for opioid toxicity among patients with OUD between Jan. 1, 2013, and Mar. 31, 2020, in Ontario, Canada. The primary outcome was OAT initiation (methadone, buprenorphine-naloxone, or slow-release oral morphine) within 7 days of discharge, measured quarterly. We examined the impact of the release of the OUD management guideline on OAT initiation rates using Autoregressive Integrated Moving Average models. Among 20 702 hospital visits for opioid toxicity among patients with OUD, the median age was 35 years, and 65.1% were male. Over the study period, the percentage of visits leading to OAT initiation within 7 days rose from 1.7% or less (Q1 2013) to 5.6% (Q1 2020); however, the publication of the Canadian OUD management guideline was not associated with a significant increase in these rates (0.14% slope change, 95% confidence interval -0.11% to 0.38%; p = 0.3). Among hospital encounters for opioid toxicity, despite rising prevalence over time, only 1 in 18 patients were dispensed OAT within a week of discharge in early 2020. These findings highlight missed opportunities to initiate therapies proven to reduce mortality in patients with OUD.

Using of Autoregressive and Seasonal Integrated Moving Averages Models to Forecasting the Maximum Temperatures for the City of Baquba

تستخدم هذه الدراسة نموذج السلاسل الزمنية الموسمية Box-Jenkins لدراسة وتحليل بيانات درجة الحرارة القصوى الشهرية لمدينة بعقوبة خلال 2021-2023. يتميز النموذج بدقة عالية ومرونة تحليلية عالية ، اذ ان اظهرت نتائج الجانب التطبيق للبحث والتي تم استخدام نماذج السلاسل الزمنية الموسمية فيها وهذه النماذج هي SARIMA(1,1,0) (1,1,2) 12 و SARIMA (1,1,0) (2,1,1) 12 و SARIMA (1,1,0) (1,1,1) 12 وغيرها من النماذج وبالاعتماد على معايير جودة توافق الانموذج (معيار معلومات اكايكي ومعيار معلومات بيز ومعيار حنان كوين ) تبين أن النموذج المناسب والفعال لتمثيل بيانات السلاسل الزمنية هو انموذج الانحدار الذاتي والمتوسطات المتحركة التكاملية الموسمية لـ SARIMA (1,1,0) (1,1,2) 12 وبناءً على تقديرات النموذج ، تم التنبؤ بدرجات الحرارة للفترة من يناير 2021 حتى ديسمبر 2023 ، حيث تتوافق القيم مع القيم المقابلة في السلسلة الزمنية الأصلية.

A Novel Short-Term Traffic Prediction Model Based on SVD and ARIMA With Blockchain in Industrial Internet of Things

With the construction and development of smart cities, accurate and real-time traffic prediction plays a vital role in urban traffic. However, traffic data has the characteristics of non-linearity, non-stationary and complex structure, so traffic prediction has always been a challenging problem. The traditional statistical model is good at dealing with linear data and poor at dealing with nonlinear data. Although the ability to capture nonlinear data has improved, the deep learning approach has difficulty in meeting the real-time requirements of traffic prediction. To solve the above challenges, we propose a novel approach based on the Autoregressive Integrated Moving Average model (ARIMA) model and combining empirical mode decomposition (EMD) and singular value decomposition (SVD) technology, i.e., ESARIMA. This method first uses EMD to stabilize the traffic data, then uses SVD to compress data and reduce the noise, so as to improve the efficiency and accuracy of ARIMA model in predicting traffic flow. Finally, we use real datasets to verify the feasibility of ESARIMA. The experimental results show that our method outperforms state-of-art baselines.

Prediction models for respiratory event types in OSA patients based on hypoxic parameters

Objective: To analyze the hypoxic parameters in patients with obstructive sleep apnea (OSA), to explore the difference and association between different types of respiratory events and to construct predictive models for respiratory event types. Methods: Fifty patients [including 41 males and 9 females with age 18-74(45.72±13.39) years ] with OSA diagnosed by polysomnography (PSG) were selected for retrospective analysis, and all respiratory events with pulse oximetry (SpO2) desaturation in the recorded overnight data were divided into hypopnea group (Hyp, 3 316), obstructive apnea group (OA, 5 552), central apnea group (CA, 1 088) and mixed apnea group (MA, 1 369) according to the type of events, and all event records were exported separately from the PSG software as comma-separated variable (.csv) files, which were imported and analyzed using the in-house built Matlab software. A total of 13 hypoxic parameter differences were compared among the four groups, including minimum oxygen saturation of events (e-minSpO2), the depth of desaturation (ΔSpO2), the duration of desaturation and resaturation (DSpO2), the duration of desaturation (d.DSpO2), duration of resaturation (r.DSpO2), duration of SpO2<90% (T90), duration of SpO2<90% during desaturation (d.T90), duration of SpO2<90% during resaturation (r.T90), area under the curve of SpO2<90% (ST90), area under the curve of SpO2<90% during desaturation (d.ST90), area under the curve of SpO2<90% during resaturation (r.ST90), oxygen desaturation rate (ODR) and oxygen resaturation rate (ORR). Hyp model (H), OA model (O), CA model (C) and MA model (M) were constructed respectively; group differences for the different hypoxia parameters were assessed using single factor analysis and Kruskal-Wallis H test. For different categories of respiratory events, binary logistic regression was used to identify the variables included in the regression model. Receiver operating characteristic (ROC) curves were generated to assess and compare the sensitivity, specificity, positive predictive value and negative predictive value of the four models, thereby gauging the predictive precision of each model. Results: ΔSpO2, ODR, ORR, T90, d.T90, r.T90, ST90, d.ST90 and r.ST90 for each type of respiratory events showed MA>OA>CA>Hyp, and e-minSpO2 showed MA<OA<CA<Hyp. Logistic regression showed that e-minSpO2, ΔSpO2, d.DSpO2, r.DSpO2, ODR, ORR, d.T90, r.T90, d.ST90 and r.ST90 were independent predictors for Hyp, ΔSpO2, d.DSpO2, r.DSpO2, ORR, d.T90, r.T90, d.ST90 and r.ST90 were independent predictors for OA, ΔSpO2, d.DSpO2, r. DSpO2, ODR, ORR, r.T90, d.ST90 and r.ST90 were independent predictors for CA, while e-minSpO2, ΔSpO2, d.DSpO2, r.T90, d.ST90 and r.ST90 were independent predictors for MA. The area under the ROC curve (AUC) of the H, O, C, and M models were 0.875, 0.751, 0.755, and 0.749, respectively, and all models had high specificity (0.865, 0.722, 1.000, and 0.993, respectively) and negative predictive values (0.871, 0.692, 0.904, and 0.881, respectively). Conclusions: Four predictive models for respiratory event types can be constructed based on hypoxic parameters, providing a feasible novel tool for applying nocturnal SpO2 to automatically identify respiratory event types.

Forecasting daily COVID-19 cases with gradient boosted regression trees and other methods: evidence from U.S. cities.

There is a vast literature on the performance of different short-term forecasting models for country specific COVID-19 cases, but much less research with respect to city level cases. This paper employs daily case counts for 25 Metropolitan Statistical Areas (MSAs) in the U.S. to evaluate the efficacy of a variety of statistical forecasting models with respect to 7 and 28-day ahead predictions. This study employed Gradient Boosted Regression Trees (GBRT), Linear Mixed Effects (LME), Susceptible, Infectious, or Recovered (SIR), and Seasonal Autoregressive Integrated Moving Average (SARIMA) models to generate daily forecasts of COVID-19 cases from November 2020 to March 2021. Consistent with other research that have employed Machine Learning (ML) based methods, we find that Median Absolute Percentage Error (MAPE) values for both 7-day ahead and 28-day ahead predictions from GBRTs are lower than corresponding values from SIR, Linear Mixed Effects (LME), and Seasonal Autoregressive Integrated Moving Average (SARIMA) specifications for the majority of MSAs during November-December 2020 and January 2021. GBRT and SARIMA models do not offer high-quality predictions for February 2021. However, SARIMA generated MAPE values for 28-day ahead predictions are slightly lower than corresponding GBRT estimates for March 2021. The results of this research demonstrate that basic ML models can lead to relatively accurate forecasts at the local level, which is important for resource allocation decisions and epidemiological surveillance by policymakers.

Landscape, Socioeconomic, and Meteorological Risk Factors for Canine Leptospirosis in Urban Sydney (2017-2023): A Spatial and Temporal Study.

Leptospirosis is a potentially fatal zoonotic disease caused by infection with pathogenic Leptospira spp. We described reported clinical cases of canine leptospirosis in the council areas of the Inner West and the City of Sydney, Australia, from December 2017 to January 2023 and tested the association with urban spatial (landscape and socioeconomic factors, community seroprevalence, and urban heat island effect) and temporal (precipitation and minimum and maximum temperature) factors and the cases using log-transformed Poisson models, spatially stratified population-adjusted conditional logistic models, General Additive Models (GAMs), and Autoregressive Integrated Moving Average (ARIMA) models. The results suggested that canine leptospirosis is now endemic in the study area. A longer distance to the nearest veterinary hospital (RR 0.118, 95% CI -4.205--0.065, p < 0.05) and a mildly compromised Index of Economic Resources (IER) (RR 0.202, 95% CI -3.124--0.079, p < 0.05) were significant protective factors against leptospirosis. In areas proximal to the clinical cases and seropositive samples, the presence of tree cover was a strong risk factor for higher odds of canine leptospirosis (OR 5.80, 95% CI 1.12-30.11, p < 0.05). As the first study exploring risk factors associated with canine leptospirosis in urban Sydney, our findings indicate a potential transmission from urban green spaces and the possibility of higher exposure to Leptospira-or increased case detection and reporting-in areas adjacent to veterinary hospitals.