A strategic priority of the World Health Organization\'s Immunization Agenda 2030 is to increase vaccination coverage and equity through reaching \"zero-dose\" children. Through an ecological study, we sought to quantify the impact of the COVID-19 pandemic on the coverage of the pentavalent and the measles/rubella vaccines in Kenya, without implying causality. The monthly number of doses from January 2017 to August 2022 were obtained from the Kenya Health Information System for the pentavalent and the measles/rubella vaccines. Immediate (step) and long-term (ramp) changes following interruptions occurring during the period from March 2020 to December 2020 were assessed through an interrupted time series analysis using an autoregressive integrated moving average (ARIMA) model, accounting for seasonality. In December 2020, there was an immediate decrease of 8337, 12,212, and 20,848 in the number of doses for the first, second, and third dose of the pentavalent vaccine, respectively (statistically significant for the third dose only). This corresponded to a percentage relative difference of -21.6, -20.1, and -24.5, respectively, for three doses of pentavalent vaccines, while for measles/rubella vaccine it was -27.3 and -33.6, respectively, for the first and second dose. COVID-19 resulted in interruptions affecting routine immunization, but recovery occurred within four months.

This study aimed to predict the annual herd milk yield, lactation, and reproductive cycle stages in a high-input dairy herd in a zone with prolonged thermal stress. Also, the impact of climatic conditions on milk yield and productive and reproductive status was assessed. An autoregressive integrated moving average (ARIMA) model was used in data fitting to predict future monthly herd milk yield and reproductive status using data from 2014 to 2020. Based on the annual total milk output, the highest predicted percentage of milk yield based on the yearly milk production was in February (9.1%; 95% CI = 8.3-9.9) and the lowest in August (6.9%; 95% CI = 6.0-7.9). The predicted highest percentage of pregnant cows for 2021 was in May (61.8; 95% CI = 53.0-70.5) and the lowest for November (33.2%; 95% CI = 19.9-46.5). The monthly percentage of dry cows in this study showed a steady trend across years; the predicted highest percentage was in September (20.1%; CI = 16.4-23.7) and the lowest in March (7.5%; 4.0-11.0). The predicted days in milk (DIM) were lower in September (158; CI = 103-213) and highest in May (220; 95% CI = 181-259). Percentage of calvings was seasonal, with the predicted maximum percentage of calvings occurring in September (10.3%; CI = 8.0-12.5) and the minimum in April (3.2%; CI = 1.0-5.5). The highest predicted culling rate for the year ensuing the present data occurred in November (4.3%; 95% CI = 3.2-5.4) and the lowest in April (2.5%; 95% CI = 1.4-3.5). It was concluded that meteorological factors strongly influenced rhythms of monthly milk yield and reproductive status. Also, ARIMA models robustly estimated and forecasted productive and reproductive events in a dairy herd in a hot environment.

Inflation is one of macroeconomic issues in Indonesia that needs to be controlled. Inflation could happen because of widespread increases in the cost of goods and services. Annual inflation rate in Indonesia on 2008 to 2023 are quite fluctuating and several periods are not achieved inflation target yet. One of the ways to control inflation is by making predictions for the upcoming period. Java Island is the biggest contributor on economy and Gross Domestic Product (GDP) in Indonesia so it can be considered as general indicator to measure overall inflation rate of Indonesia. Thus, data used in this study is monthly inflation at each province in Java Island from January 2008 to December 2023. This study using two methods, Autoregressive Integrated Moving Average (ARIMA) for univariate time series prediction and Generalized Space-Time ARIMA (GSTARIMA) for multivariate time series prediction with a spatial factor. The results of both models will be compared to determine which model has better accuracy. Based on RMSE value, GSTARIMA model has least average RMSE value, which is 0.113 compared with ARIMA model which has average RMSE value 0.319 thus it can conclude that spatial factors addition could increase accuracy on inflation prediction in Java Island.•This paper purposes to get Java Island\'s inflation rate prediction to determine better policy on controlling cost of goods and services.•Best model using GSTARIMA methods is GSTARMA(1,1) with distance invese matrix that indicate that coordinate point of each location increase performance of inflation rate prediction.•The result indicate GSTARIMA has better accuracy than ARIMA for inflation prediction in Java Island based on RMSE value.

BACKGROUND: During the COVID-19 pandemic, the Chinese government implemented nationwide public health interventions to control its spread. However, the impact of these measures on other infectious diseases remains unclear.
METHODS: The incidence of three types of notifiable infectious diseases in China were analyzed between 2013 and 2021. The seasonal Mann-Kendall test and Mann-Kendall mutation test were employed to examine trends and mutations in the time series. Based on the counterfactual inference, historical incidence rates were employed to construct SARIMA models and predict incidence between January 2020 and December 2021. Differences between reported and predicted incidences during the pandemic were compared using the Mann-Whitney U test.
RESULTS: Between 2013 and 2019, the incidence rate of three types of notifiable infectious diseases fluctuated between 494.05/100,000 and 550.62/100,000. No discernible trend was observed for types A and B infectious diseases (Z = -1.344, P = 0.18). A significant upward trend was observed for type C infectious diseases (Z = 2.56, P = 0.01). In 2020, the overall incidence rate of three types of notifiable infectious diseases decreased to 367.08/100,000. Compared to predicted values, the reported incidence of three types of infectious diseases was, on average, 30.05% lower in 2020 and 16.58% lower in 2021.
CONCLUSIONS: The public health interventions implemented during the pandemic had a positive consequence on the prevention and control of other infectious diseases, with a particularly notable effect on type C infectious diseases. Among the diseases with different transmission routes, respiratory diseases and gastrointestinal or enteroviral diseases decreased significantly.

BACKGROUND: This study aims to predict the trend of procurement and storage of various blood products, as well as planning and monitoring the consumption of blood products in different centers across Iran based on artificial intelligence until the year 2027.
METHODS: This research constitutes a time-series investigation within the realm of longitudinal studies. In this study, information on the number of packed red blood cells (RBC), leukoreduced red blood cells (LR-RBC), and platelets (PLT), PLT-Apheresis, and fresh frozen plasma (FFP) was requested from all blood transfusion centers in the country and extracted using a unified protocol. After the initial examination of the information and addressing data issues and inconsistencies, the corrected data were analyzed. Both conventional and artificial intelligence approaches were used to predict each product in this study. The best model was selected based on goodness-of-fit indicators RMSE and MAPE.
RESULTS: Based on the obtained results, the FFP product will follow a relatively consistent process similar to previous years in the next five years. The PLT product is predicted to have a growing trend over the next 5 years, which applies to both the demand and supply of the product. The PLT-Apheresis product also shows a similar upward trend, albeit with a lower growth rate. The RBC product will have a constant trend over a 5-year period (long-term) according to both models, taking into account short-term changes. Similarly, there is a similar trend in LR-RBC, with the expectation that short-term pattern repetition will continue over a 5-year period (long-term). Comparing the goodness-of-fit results, the LSTM model proved to be better for predicting the dominant blood products.
CONCLUSIONS: The growth of the elderly population and diseases related to old age, and on the other hand, the trend of increasing the consumption of the product with a short lifespan (PLT) requires the activation of the management of the patient\'s blood, especially in relation to this product in medical centers. The trend for other products in the next five years is similar to previous years, and no growth in demand is observed. The LSTM method, considering periodic and cyclical events, has performed the prediction.

This study aims to explore the application value of the Bayesian Time Structure Sequence (BSTS) model in estimating the acute hemorrhagic conjunctivitis (AHC) epidemics. The reported AHC cases spanning from January 2011 to October 2022 in China were collated. Utilizing R software, the BSTS and Autoregressive Integrated Moving Average (ARIMA) models were constructed using the data from January 2011 to December 2021. The prediction effect of both models was compared using the data from January to October 2022, and finally the AHC incidence from November 2022 to December 2023 was predicted. The results indicated that forecast errors under the BSTS model were lower than those under the ARIMA model. The actual AHC incidence in July 2022 from the ARIMA model deviated from the 95% confidence interval (CI) of the predicted value. However, the observed AHC incidence from the BSTS model fell within the 95% CI of the predicted value. Notably, the BSTS model predicted 26,474 new AHC cases in China from November 2022 to December 2023, exhibiting better prediction performance compared to the ARIMA model. This indicates that the BSTS model possesses a high application value for forecasting the epidemic trends of AHC, making it a valuable tool for disease surveillance and prevention strategies.

UNASSIGNED: Public health surveillance is an important aspect of outbreak early warning based on prediction models. The present study compares a hybrid model based on discrete wavelet transform (DWT) and ARIMA (Autoregressive Integrated Moving Average) for predicting incidence cases due to COVID-19.
UNASSIGNED: In the current cross-sectional stuady based on time-series data, the incidence data for confirmed daily cases of COVID-19 from February 26, 2019, to April 25, 2022, were used. A hybrid model based on DWT and ARIMA and a pure ARIMA model were used to predict the trend. All analyzes were performed by MATLAB 2018, stata 2015, and Excel 2013 computer software.
UNASSIGNED: Compared to the ARIMA model, the prediction results of the hybrid model were closer to the actual number of incident cases. The correlation between predicted values by the hybrid model with real data was higher than the correlation between predicted values by the ARIMA model with actual data.
UNASSIGNED: Discreet Wavelet decomposition of the dataset was combined with an ARIMA model and showed better performance in predicting the future trend.

UNASSIGNED: Rescuing individuals at sea is a pressing global public health issue, garnering substantial attention from emergency medicine researchers with a focus on improving prevention and control strategies. This study aims to develop a Dynamic Bayesian Networks (DBN) model utilizing maritime emergency incident data and compare its forecasting accuracy to Auto-regressive Integrated Moving Average (ARIMA) and Seasonal Auto-regressive Integrated Moving Average (SARIMA) models.
UNASSIGNED: In this research, we analyzed the count of cases managed by five hospitals in Hainan Province from January 2016 to December 2020 in the context of maritime emergency care. We employed diverse approaches to construct and calibrate ARIMA, SARIMA, and DBN models. These models were subsequently utilized to forecast the number of emergency responders from January 2021 to December 2021. The study indicated that the ARIMA, SARIMA, and DBN models effectively modeled and forecasted Maritime Emergency Medical Service (EMS) patient data, accounting for seasonal variations. The predictive accuracy was evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Coefficient of Determination (R 2) as performance metrics.
UNASSIGNED: In this study, the ARIMA, SARIMA, and DBN models reported RMSE of 5.75, 4.43, and 5.45; MAE of 4.13, 2.81, and 3.85; and R 2 values of 0.21, 0.54, and 0.44, respectively. MAE and RMSE assess the level of difference between the actual and predicted values. A smaller value indicates a more accurate model prediction. R 2 can compare the performance of models across different aspects, with a range of values from 0 to 1. A value closer to 1 signifies better model quality. As errors increase, R 2 moves further from the maximum value. The SARIMA model outperformed the others, demonstrating the lowest RMSE and MAE, alongside the highest R 2, during both modeling and forecasting. Analysis of predicted values and fitting plots reveals that, in most instances, SARIMA\'s predictions closely align with the actual number of rescues. Thus, SARIMA is superior in both fitting and forecasting, followed by the DBN model, with ARIMA showing the least accurate predictions.
UNASSIGNED: While the DBN model adeptly captures variable correlations, the SARIMA model excels in forecasting maritime emergency cases. By comparing these models, we glean valuable insights into maritime emergency trends, facilitating the development of effective prevention and control strategies.

BACKGROUND: Lung cancer (LC) is currently the number one malignancy death rate disease in China, and its disease burden is serious. The study aimed to analyze trends of LC and its risk factor attributable disease in China from 1990 to 2019 and predict the next 41 years.
METHODS: The average annual percentage change (AAPC) was used to analyze the trend of LC and its risk factor attributable incidence, deaths, and disability-adjusted life years (DALYs) rate in China from 1990 to 2019, collected in the Global Burden of Disease 2019. Cochran-Armitage trends examine trends in lung cancer disease burden by sex, age, and attributable risk factor groups in China from 1990 to 2019. In addition, based on data on death and DALYs rate due to LC and its risk factors between 1990 and 2019, an autoregressive integrated moving average (ARIMA) model was developed to predict the change in the trend of burden of disease due to LC and its risk factors over the next 41 years, and the model was evaluated using the model parameters root mean square error, mean absolute error, and mean absolute percentage error.
RESULTS: From 1990 to 2019, the incidence, mortality and DALYs of LC were all increased. Among the eight risk factors associated with lung cancer, the DALYs rate and mortality rate of lung cancer risk factors for Chinese residents increased from 1990 to 2019, except for household air pollution from solid fuels and diet low in fruit, which showed a decrease; among them, the DALYs rate and mortality rate due to ambient particulate matter pollution showed the greatest increase with AAPC values of 2.880 and 3.310, respectively, while DALYs and mortality rates due to household air pollution from solid fuels showed the largest decreases, with AAPC values of -4.755 and -4.348, respectively. The results of the ARIMA model predictions show that both the mortality rate and the rate of DALYs for lung cancer are increasing yearly, and it is predicted that the rate of DALYs for lung cancer by 2060 will reach 740.095/100 000 and the mortality rate will reach 35.151/100 000. It is expected that by 2060, the top four risk factors for lung cancer in China will be, in order of DALYs rate and mortality rate, smoking, ambient particulate matter pollution, high fasting plasma glucose (HFPG), and secondhand smoke, with HFPG showing the greatest increase.
CONCLUSIONS: The LC burden increased from 1990 to 2019 in China, the LC burden that could be attributed to HFPG will continue to increase in the next 40 years, and will be the third most factor by 2060. Targeted interventions are warranted to facilitate the prevention of LC and improvement of health-related quality of life patients with LC.

UNASSIGNED: Model prediction of radioactivity levels around nuclear facilities is a useful tool for assessing human health risks and environmental impacts. We aim to develop a model for forecasting radioactivity levels in the environment and food around the world\'s first AP 1000 nuclear power unit.
UNASSIGNED: In this work, we report a pilot study using time-series radioactivity monitoring data to establish Autoregressive Integrated Moving Average (ARIMA) models for predicting radioactivity levels. The models were screened by Bayesian Information Criterion (BIC), and the model accuracy was evaluated by mean absolute percentage error (MAPE).
UNASSIGNED: The optimal models, ARIMA (0, 0, 0) × (0, 1, 1)4, and ARIMA (4, 0, 1) were used to predict activity concentrations of 90Sr in food and cumulative ambient dose (CAD), respectively. From the first quarter (Q1) to the fourth quarter (Q4) of 2023, the predicted values of 90Sr in food and CAD were 0.067-0.77 Bq/kg, and 0.055-0.133 mSv, respectively. The model prediction results were in good agreement with the observation values, with MAPEs of 21.4 and 22.4%, respectively. From Q1 to Q4 of 2024, the predicted values of 90Sr in food and CAD were 0.067-0.77 Bq/kg and 0.067-0.129 mSv, respectively, which were comparable to values reported elsewhere.
UNASSIGNED: The ARIMA models developed in this study showed good short-term predictability, and can be used for dynamic analysis and prediction of radioactivity levels in environment and food around Sanmen Nuclear Power Plant.