Time series analysis of bovine venereal diseases in La Pampa, Argentina

Predicting ambient PM2.5 concentrations via time series models in Anhui Province, China

Due to rapid expansion in the global economy and industrialization, PM2.5 (particles smaller than 2.5 µm in aerodynamic diameter) pollution has become a key environmental issue. The public health and social development directly affected by high PM2.5 levels. In this paper, ambient PM2.5 concentrations along with meteorological data are forecasted using time series models, including random forest (RF), prophet forecasting model (PFM), and autoregressive integrated moving average (ARIMA) in Anhui province, China. The results indicate that the RF model outperformed the PFM and ARIMA in the prediction of PM2.5 concentrations, with cross-validation coefficients of determination R2, RMSE, and MAE values of 0.83, 10.39 µg/m3, and 6.83 µg/m3, respectively. PFM achieved the average results (R2 = 0.71, RMSE = 13.90 µg/m3, and MAE = 9.05 µg/m3), while the predicted results by ARIMA are comparatively poorer (R2 = 0.64, RMSE = 15.85 µg/m3, and MAE = 10.59 µg/m3) than RF and PFM. These findings reveal that the RF model is the most effective method for predicting PM2.5 and can be applied to other regions for new findings.

Evaluating swine disease occurrence on farms using the state-space model based on meat inspection data: a time-series analysis

BACKGROUND

Data on abnormal health conditions in animals obtained from slaughter inspection are important for identifying problems in fattening management. However, methods to objectively evaluate diseases on farms using inspection data has not yet been well established. It is important to assess fattening management on farms using data obtained from slaughter inspection. In this study, we developed the state-space model to evaluate swine morbidity using slaughter inspection data.

RESULTS

The most appropriate model for each disease was constructed using the state-space model. Data on 11 diseases in slaughterhouses over the past 4 years were used to build the model. The model was validated using data from 14 farms. The local-level model (the simplest model) was the best model for all diseases. We found that the analysis of slaughter data using the state-space model could construct a model with greater accuracy and flexibility than the ARIMA model. In this study, no seasonality or trend model was selected for any disease. It is thought that models with seasonality were not selected because diseases in swine shipped to slaughterhouses were the result of illness at some point during the 6-month fattening period between birth and shipment.

CONCLUSION

Evaluation of previous diseases helps with the objective understanding of problems in fattening management. We believe that clarifying how farms manage fattening of their pigs will lead to improved farm profits. In that respect, it is important to use slaughterhouse data for fattening evaluation, and it is extremely useful to use mathematical models for slaughterhouse data. However, in this research, the model was constructed on the assumption of normality and linearity. In the future, we believe that we can build a more accurate model by considering models that assume non-normality and non-linearity.

Spatial-temporal trends and economic losses associated with bovine abortifacients in central Argentina

The aims of this work are, firstly, to provide the geolocalization of cases of bovine abortion with definitive diagnosis and, secondly, to estimate the economic losses due to the most frequent abortifacients diagnosed agents in cattle in Buenos Aires province, Argentina. The total beef and dairy cattle population at risk of abortion is 8,358,186 and 538,076, respectively. In beef cattle, the overall risk of abortion was estimated at 4.5% for all pregnancies, where 27.9% are due to Campylobacter fetus, Neospora caninum, Leptospira spp., Brucella abortus, and bovine viral diarrhea virus with economic losses of US$ 440 per abortion, being the annual loss to the beef industry of US$ 50,144,101. In dairy cattle, there was an 8.0% risk of suffering abortion, 26.1% produced by the same abortigenic agents. The economic losses were estimated at US$ 1,415 per abortion, which equals a total loss of US$ 17,298,498 for the dairy industry in the region. The results of this study show that infectious causes are highly prevalent in Buenos Aires province, and they caused severe economic impacts in the dairy and beef industries. Furthermore, changes in temporal trends of infectious abortion occurrence were detected, probably related to the inclusion of molecular diagnostic techniques with more sensitivity or different epidemiological or husbandry conditions in the region analyzed.

Forecasting disability-adjusted life years for chronic diseases: reference and alternative scenarios of salt intake for 2017-2040 in Japan

BACKGROUND

In Japan, a high-sodium diet is the most important dietary risk factor and is known to cause a range of health problems. This study aimed to forecast Japan's disability-adjusted life year (DALYs) for chronic diseases that would be associated with high-sodium diet in different future scenarios of salt intake. We modelled DALY forecast and alternative future scenarios of salt intake for cardiovascular diseases (CVDs), chronic kidney diseases (CKDs), and stomach cancer (SC) from 2017 to 2040.

METHODS

We developed a three-component model of disease-specific DALYs: a component on the changes in major behavioural and metabolic risk predictors including salt intake; a component on the income per person, educational attainment, and total fertility rate under 25 years; and an autoregressive integrated moving average model to capture the unexplained component correlated over time. Data on risk predictors were obtained from Japan's National Health and Nutrition Surveys and from the Global Burden of Disease Study 2017. To generate a reference forecast of disease-specific DALY rates for 2017-2040, we modelled the three diseases using the data for 1990-2016. Additionally, we generated better, moderate, and worse scenarios to evaluate the impact of change in salt intake on the DALY rate for the diseases.

RESULTS

In our reference forecast, the DALY rates across all ages were predicted to be stable for CVDs, continuously increasing for CKDs, and continuously decreasing for SC. Meanwhile, the age group-specific DALY rates for these three diseases were forecasted to decrease, with some exceptions. Except for the ≥70 age group, there were remarkable differences in DALY rates between scenarios, with the best scenario having the lowest DALY rates in 2040 for SC. This represents a wide scope of future trajectories by 2040 with a potential for tremendous decrease in SC burden.

CONCLUSIONS

The gap between scenarios provides some quantification of the range of policy impacts on future trajectories of salt intake. Even though we do not yet know the policy mix used to achieve these scenarios, the result that there can be differences between scenarios means that policies today can have a significant impact on the future DALYs.

SutteARIMA: Short-term forecasting method, a case: Covid-19 and stock market in Spain

This study aimed to predict the short-term of confirmed cases of covid-19 and IBEX in Spain by using SutteARIMA method. Confirmed data of Covid-19 in Spanish was obtained from Worldometer and Spain Stock Market data (IBEX 35) was data obtained from Yahoo Finance. Data started from 12 February 2020-09 April 2020 (the date on Covid-19 was detected in Spain). The data from 12 February 2020-02 April 2020 using to fitting with data from 03 April 2020 - 09 April 2020. Based on the fitting data, we can conducted short-term forecast for 3 future period (10 April 2020 - 12 April 2020 for Covid-19 and 14 April 2020 - 16 April 2020 for IBEX). In this study, the SutteARIMA method will be used. For the evaluation of the forecasting methods, we applied forecasting accuracy measures, mean absolute percentage error (MAPE). Based on the results of ARIMA and SutteARIMA forecasting methods, it can be concluded that the SutteARIMA method is more suitable than ARIMA to calculate the daily forecasts of confirmed cases of Covid-19 and IBEX in Spain. The MAPE value of 0.036 (smaller than 0.03 compared to MAPE value of ARIMA) for confirmed cases of Covid-19 in Spain and was in the amount of 0.026 for IBEX stock. At the end of the analysis, this study used the SutteARIMA method, this study calculated daily forecasts of confirmed cases of Covid-19 in Spain from 10 April 2020 until 12 April 2020 i.e. 158925; 164390; and 169969 and Spain Stock Market from 14 April 2020 until 16 April 2020 i.e. 7000.61; 6930.61; and 6860.62.

Forecasting PM10 concentrations using time series models: a case of the most polluted cities in Turkey

Particulate matter (PM), which is one of the most important parameters in the area of air pollution, has widespread impacts on human health. Hence, the prediction of the probable concentration of PM is a highly significant subject with regard to primary warning for the protection of a population. Turkey is among the European countries with polluted air in terms of the concentration of PM with a diameter smaller than 10 μ m (PM₁₀). The PM₁₀ data supplies significant knowledge about how much pollution is in the air and which city is the most polluted. In this study, the values of PM₁₀ for the most polluted cities in Turkey are forecasted using time series models, including autoregressive integrated moving average (ARIMA), error, trend and seasonal (ETS), and singular spectrum analysis (SSA). Forecast values of PM₁₀ averaging period of 24 h for the year 2019 are obtained using SSA as the optimum time series method. The results show that the annual means of PM₁₀ concentrations in 2019 in Hatay and Yalova, the most polluted cities, will not exceed the 50 μgm^- 3 value according to air quality standards determined by the European Commission. The air quality levels of eight other cities, which are Adana, Ankara, Icel, Istanbul, Kirklareli, Sakarya, Samsun, and Sivas, will reach acceptable standards between 50 and 70 μgm^- 3 for annual mean in 2019. The remaining eight cities, Amasya, Bursa, Denizli, Kahramanmaras, Kutahya, Manisa, Nigde, and Tekirdag, continue to be the most polluted cities in 2019 according to the average annual PM₁₀ values. This study also reveals that the average PM₁₀ value of the most polluted cities in Turkey will be 68.97 μgm^- 3 for the 24-h average in 2019.

Major factors associated to persistence of bovine trichomoniasis in a mandatory control plan: A eight year retrospective study in La Pampa, Argentine

Bovine trichomoniasis is a venereal disease caused by the flagellate protozoan Tritrichomonas foetus. Infection is related to low conception rates and would have significant impact on calf crop. The state of La Pampa started in 2006 an unprecedented mandatory control program for eradication of bovine trichomoniasis. The compulsory participation of all cattle producers and the yearly control of every bull should be followed by culling of every positive animal. This retrospective study on data from eight years of the control plan showed that 80% of farms had a single year of positive tests. In these farms, positive tests showed a strong decay of disease during the first years that reached a baseline by 2012. A non negligeable proportion of positive bulls in this group can be attributed to false positive tests. Oppositely, farms with two or more years of positive diagnosis accounted for a great proportion of recent cases. These farms were more likely related to less intensive control measures. The non exclusion of carrier bulls is the major factor contributing to the persistance of bovine trichomoniasis.

Diagnosis of Bovine Genital Campylobacteriosis in South America

Bovine genital campylobacteriosis (BGC) is a venereal infectious disease that affects reproduction. It is caused by the Gram-negative bacillus Campylobacter fetus subspecies venerealis (Cfv), which may include the biotype intermedius. The bull is a lifelong asymptomatic carrier and transmitter of the disease. In females Cfv may cause infertility and sporadic abortion. The objective of this study is to review and discuss methods for the diagnosis of BGC, its prevalence and economic impact in South America. BGC is a worldwide distributed disease and can cause a pregnancy rate decrease of 15-25%. The farm prevalence of BGC in different regions of South American countries shows a variation between 2.3 and 100%. Discrepancies may depend on the differences on sanitary, management, and reproductive practices between farms and regions, but also on the interpretation of different diagnostic tests. Currently known laboratory tests include bacterial culture, direct immunofluorescence, immunoenzymatic assays, vaginal mucus agglutination test, PCR-based methods, histology and immunohistochemistry, which are applied and interpreted in diagnostic laboratories at different scales. Epidemiologic data of BGC in South America should be interpreted with caution. High prevalence has been reported in some studies, although the low specificity of the diagnostic tests used could lead to an overestimation of the results.