Time-series analysis of population dynamics of the common cutworm, Spodoptera litura (Lepidoptera: Noctuidae), using an ARIMAX model

Data-driven approach to weekly forecast of the western flower thrips (Frankliniella occidentalis Pergande) population in a pepper greenhouse with an ensemble model

BACKGROUND

Integrated pest management (IPM) in European glasshouses has substantially advanced in automated insect pest detection systems lately. However, transforming such an enormous data influx into optimal biological control strategies remains challenging. In addition, most biological control forecast studies relied on the single-best model approach, which is susceptible to overconfidence, and they lack validation over sufficient sampling repetitions where robustness remains questionable. Here we propose employing an unweighted ensemble model, by combining multiple forecasting models ranging from simple models (linear regressions and Lotka-Volterra model) to machine learning models (Gaussian process, Random Forest, XGBoost, Multi-Layer Perceptron), to predict 1-week-ahead population of western flower thrips (Frankliniella occidentalis), a notorious pest in glasshouses, under the influence of its biological control agent Macrolophus pygmaeus in pepper-growing glasshouses.

RESULTS

Models were trained with only 1 year of data, validated over 3 years of monitoring of multiple compartments to evaluate their robustness. The full ensemble model outperformed the Naïve Forecast in 10 out of 14 compartments for validation, with around 0.451 and 26.6% increase in coefficient of determination (R2) and directional accuracy, respectively. It also extended 0.096 in R2 from the best single model, equivalent to a 27% increase in accuracy, while maintaining a 75% directional accuracy.

CONCLUSION

Our results demonstrated the benefits of the ensemble model over the traditional 'single-best model' approach, avoiding model structural biases and minimizing the risk of overconfidence. This showcased how an ensemble model with minimal training data can assist growers in fully utilizing the pest monitoring data and support their decision-making on IPM. © 2025 Society of Chemical Industry.

The Efficiency of Pest Control Options against Two Major Sweet Corn Ear Pests in China

Helicoverpa armigera (Hübner) and Ostrinia furnacalis (Guenée) are the most devastating insect pests at the ear stage of maize, causing significant losses to the sweet corn industry. Pesticide control primarily relies on spraying during the flowering stage, but the effectiveness is inconsistent since larvae are beneath husks within hours to a day, making pesticide treatments simpler to avoid. Insufficient understanding of pest activity patterns impedes precise and efficient pesticide control. H. armigera and O. furnacalis in corn fields were monitored in the last few years in Beijing China, and we observed a higher occurrence of both moths during the R1 stage of sweet corn. Moth captures reached the maximum during this stage, with 555-765 moths per hectare corn field daily. The control efficiency of nine synthetic insecticides and five biopesticides was assessed in the field during this period. Virtako, with mineral oil as the adjuvant, appeared to be the most effective synthetic insecticide, with the efficiencies reaching 88% and 87% on sweet and waxy corn, respectively. Pesticide residue data indicated that the corn is safe after 17 days of its use. The most effective bioinsecticide was Beauveria bassiana combined with mineral oil, with 88% and 80% control efficiency in sweet and waxy corn, respectively. These results suggested that spraying effective insecticides 5 days after corn silking could effectively control corn ear pests H. armigera and O. furnacalis. Our findings provide valuable insights for the development of ear pest management strategies in sweet corn.

Predicting monthly hospital outpatient visits based on meteorological environmental factors using the ARIMA model

Accurate forecasting of hospital outpatient visits is beneficial to the rational planning and allocation of medical resources to meet medical needs. Several studies have suggested that outpatient visits are related to meteorological environmental factors. We aimed to use the autoregressive integrated moving average (ARIMA) model to analyze the relationship between meteorological environmental factors and outpatient visits. Also, outpatient visits can be forecast for the future period. Monthly outpatient visits and meteorological environmental factors were collected from January 2015 to July 2021. An ARIMAX model was constructed by incorporating meteorological environmental factors as covariates to the ARIMA model, by evaluating the stationary [Formula: see text], coefficient of determination [Formula: see text], mean absolute percentage error (MAPE), and normalized Bayesian information criterion (BIC). The ARIMA [Formula: see text] model with the covariates of [Formula: see text], [Formula: see text], and [Formula: see text] was the optimal model. Monthly outpatient visits in 2019 can be predicted using average data from past years. The relative error between the predicted and actual values for 2019 was 2.77%. Our study suggests that [Formula: see text], [Formula: see text], and [Formula: see text] concentration have a significant impact on outpatient visits. The model built has excellent predictive performance and can provide some references for the scientific management of hospitals to allocate staff and resources.

Non-pharmaceutical interventions and urban vehicle mobility in Seoul during the COVID-19 pandemic

Non-pharmaceutical interventions to control human mobility are important in preventing COVID-19 transmission. These interventions must also help effectively control the urban mobility of vehicles, which can be a safer travel mode during the pandemic, at any time and place. However, few studies have identified the effectiveness of vehicle mobility in terms of time and place. This study demonstrates the effectiveness of non-pharmaceutical interventions at both local and national levels on intra- and inter-urban vehicle mobility by time of day in Seoul, South Korea, by applying the autoregressive integrated moving average with exogenous variables. The study found that social distancing measures at the national level were effective for intra-urban vehicle mobility, especially at night-time, but not for inter-urban mobility. Information provision with emergency text messages by cell phone was effective in reducing vehicle mobility in daytime and night-time, but not during morning peak hours. At the local level, both restrictions on late-night transit operations and stricter social distancing measures were mostly significant in reducing night-time mobility only in intra-urban areas. The study also indicates when (what time of the day), where (which area within the city), and which combination strategy could be more effective in containing urban vehicle mobility. This study recommends that restrictions on human mobility should also be extended to vehicle mobility, especially in inter-urban areas and during morning peak hours, by systematically designing diverse non-pharmaceutical interventions.