Massey Research Online - Browsing by Author "Wada M"

Browsing by Author "Wada M"

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Accelerating Disease Model Parameter Extraction: An LLM-Based Ranking Approach to Select Initial Studies for Literature Review Automation
(MDPI (Basel, Switzerland), 2025-03-26) Sujau M; Wada M; Vallée E; Hillis N; Sušnjak T; Verspoor K
As climate change transforms our environment and human intrusion into natural ecosystems escalates, there is a growing demand for disease spread models to forecast and plan for the next zoonotic disease outbreak. Accurate parametrization of these models requires data from diverse sources, including the scientific literature. Despite the abundance of scientific publications, the manual extraction of these data via systematic literature reviews remains a significant bottleneck, requiring extensive time and resources, and is susceptible to human error. This study examines the application of a large language model (LLM) as an assessor for screening prioritisation in climate-sensitive zoonotic disease research. By framing the selection criteria of articles as a question–answer task and utilising zero-shot chain-of-thought prompting, the proposed method achieves a saving of at least 70% work effort compared to manual screening at a recall level of 95% (NWSS 95%). This was validated across four datasets containing four distinct zoonotic diseases and a critical climate variable (rainfall). The approach additionally produces explainable AI rationales for each ranked article. The effectiveness of the approach across multiple diseases demonstrates the potential for broad application in systematic literature reviews. The substantial reduction in screening effort, along with the provision of explainable AI rationales, marks an important step toward automated parameter extraction from the scientific literature.
Development of LIME-NZ: a generic tool for prompt estimation of economic impacts of disease for New Zealand livestock.
(Taylor and Francis Group, 2024-02-04) Wada M; Compton C; Hickson R; Bingham P
AIMS: To develop a simple and robust generic tool to measure the impacts of livestock diseases on New Zealand dairy, beef and sheep farms using enterprise gross margin models. METHODS: The most recent (2018-2020) livestock production benchmarking data was extracted from industry-led economic surveys. Gross margin models were built for each enterprise type, accounting for 11 dairy farm types and 16 farm types for beef and sheep. Disease parameters, including changes in mortality, reproduction performance, milk yield, price of animals and culling rate, as well as additional expenses for veterinary intervention, were applied to the infected compartment of the herd/flock using the assumed annual within-herd disease incidence. Farm-level disease impacts were estimated as the difference in annual profit between the baseline and infected farm. The baseline gross margin models were validated against the industry data. The disease impact models were validated using a recently published study on bovine viral diarrhoea (BVD). The impact assessment tool, LIME-NZ, was developed using the statistical software R and implemented in the web-based R package Shiny. The input parameters can be varied interactively to obtain a range of disease impacts for uncertain disease parameters. RESULTS: The baseline gross margin models demonstrated reasonable accuracy with a mean percentage error of <14% when compared with the industry reports. The estimated annual impacts of BVD were comparable to those reported in the BVD study, NZ$38.5-140.4 thousand and $0.9-32.6 thousand per farm per year for dairy and beef enterprises, respectively. CONCLUSIONS: LIME-NZ can be used to rapidly obtain the likely economic impacts of diseases that are endemic, recently introduced or at increased risk of introduction in the New Zealand context. This will aid communication and decision-making among government agencies and the livestock industry, including veterinarians and livestock producers, about the management of diseases, until refined information becomes available to improve decision-making.
Modelling the spread of Salmonid Rickettsial Septicaemia and assessing regional control strategies for saltwater salmon farms in Aysén, Chile
(Elsevier B V, 2025-10-01) Lam CT; Ivanek R; Wada M; Rosanowski S; Nekouei O; Getchell R; St-Hilaire S
Salmonid Rickettsial Septicaemia (SRS) is a highly infectious, endemic disease that spreads among saltwater salmonid farms in Chile, making control efforts challenging. Here, we present a model that simulates SRS transmission between saltwater salmonid farms in the Aysén region of Chile, using the state-transition model framework InterSpread Plus (ISP). In ISP, the status (e.g., susceptible or infectious) of farms is individually defined, and the simulation determines the transition of the farms’ state over time. Farm characteristics, such as fish species and production size, were incorporated into the model. The model parameters were estimated based on data collected from 432 farms between 2011 and 2020, expert opinions, and literature reviews. The simulation included an average of 150 active farms per week, with a total of 46,380 active farm-weeks. The model had a one-year simulation period and was used to simulate the annual spread of P. salmonis between salmonid farms for each year from 2013 to 2019 (i.e., six one-year model simulations, each starting in September of the respective year). Model accuracy was estimated based on 1 minus the cumulative difference between the simulated and observed weekly SRS prevalence at the regional level. The average annual model accuracy for the six one-year models was 95.0 % (range: 91.7 – 96.0 %). The baseline model was used to explore a total of 19 “what-if” control scenarios addressing one of the three following strategies: (i) depopulation of infected farms, (ii) reducing number of farms in neighbourhoods, and (iii) vaccination. The impact of these scenarios was assessed based on the estimated annual incidence rate of SRS over 6 years (i.e., 2013–2019). The three most effective scenarios for reducing the annual incidence rate of SRS were: (i) immediate depopulation of infected farms, (ii) strategic removal of 30 % of highly connected farms per neighbourhood, and (iii) industry-wide implementation of a hypothetical vaccine with at least 75 % efficacy for a minimum of 9 months. However, the estimated number of fish harvested and the estimated use of antibiotics for each of these control scenarios, compared to the baseline models, suggested that they may not be cost-effective for the industry. Future research should include cost-effectiveness analyses to identify the most economical measures for the industry. The ISP model in this study introduces a novel application of this software for managing SRS in the Chilean Aysén region and could serve as a decision-support tool for policymakers, epidemiologists and fish health professionals.
Probability of freedom from foot-and-mouth disease virus serotype Asia 1 in Southeast Asia, China and Mongolia
(Elsevier B V, 2025-11-01) Wada M; Han J-H; Purevsuren B; Rinzin K; Sutar A; Abila R; Subharat S
Foot-and-mouth disease virus (FMDV) serotype Asia 1 has not been reported in Southeast Asia, China and Mongolia between 2018 and 2024, despite the endemicity of FMD in this region and the continued circulation of serotype Asia 1 in South Asia. While vaccines against Asia 1 are still occasionally used in this region, it is unknown whether the absence of reports indicates true disease freedom or surveillance gaps. This study aimed to estimate the sensitivity of existing passive surveillance systems, and the probability of regional freedom from serotype Asia 1 across eight countries using the scenario tree approach. Two stochastic scenario tree models were developed to estimate surveillance sensitivity for FMD (any serotypes) and serotype Asia 1 specifically. Country-specific input parameters were derived from a questionnaire survey of in-country experts on FMD vaccination practices, smallholders’ behaviour, sampling protocols and diagnostic laboratory capacity. Additionally, 2010 – 2022 data on FMD clinical samples submitted and confirmed Asia 1 cases were obtained from the World Reference Laboratory for FMD. Under a design annual incidence rate of 10 % at the village level and 20 % at the animal level, estimated surveillance sensitivity for FMD ranged from 100.0 % in Mongolia and 95.9 % in China to 1.7 % in Cambodia and < 0.1 % in Myanmar. Using the effective design incidence rate with a median of 0.02 – 0.07 % at the village level and 20 % at the animal level, the probability of detecting Asia 1 was estimated to be 0.0 – 6.7 % per country and 14.5 % for the region. The estimated probability of regional freedom from Asia 1 was 53.9 % after the first year without reporting. Over years of no reporting, this probability would increase, only if an annual risk of introduction remained below 6 %. The results were most sensitive to parameters related to sampling intensity and smallholders’ behaviour, particularly in countries with high surveillance sensitivity, such as Mongolia and China. Our findings highlight the low sensitivity of passive surveillance in the region, suggesting that serotype Asia 1 may have remained undetected under the current surveillance efforts. Strengthening data collection and continued efforts in increasing surveillance intensity are essential to improving confidence in the regional freedom from serotype Asia 1.
The association between rainfall and human leptospirosis in Aotearoa New Zealand
(Cambridge University Press, 2025-08-26) Tana T; Wada M; Benschop J; Vallee E
Leptospirosis remains a significant occupational zoonosis in New Zealand, and emerging serovar shifts warrant a closer examination of climate-related transmission pathways. This study aimed to examine whether total monthly rainfall is associated with reported leptospirosis in humans in New Zealand. Poisson and negative binomial models were developed to examine the relationship between rainfall at 0-, 1-, 2-, and 3-month lags and the incidence of leptospirosis during the month of the report. Total monthly rainfall was positively associated with the occurrence of human leptospirosis in the following month by a factor of 1.017 (95% CI: 1.007–1.026), 1.023 at the 2-month lag (95% CI:1.013–1.032), and 1.018 at the 3-month lag (95% CI: 1.009–1.028) for every additional cm of rainfall. Variation was present in the magnitude of association for each of the individual serovars considered, suggesting different exposure pathways. Assuming that the observed associations are causal, this study supports that additional human cases are likely to occur associated with increased levels of rainfall. This provides the first evidence for including rainfall in a leptospirosis early warning system and to design targeted communication and prevention measures and provide resource allocation, particularly after heavy rainfall in New Zealand.
The Effect of Climate Change on Emergence and Evolution of Zoonotic Diseases in Asia
(Wiley-VCH GmbH, 2025-09-01) Morris RS; Wada M
As the climate of Asia changes under the influence of global warming, the incidence and spatial distribution of known zoonoses will evolve, and new zoonoses are expected to emerge as a result of greater exposure to organisms which currently occur only in wildlife. In order to evaluate the risks attached to different transmission methods and organism maintenance mechanisms, a classification system is provided which allocates diseases into nine epitypes. All animal diseases and zoonoses recognised as globally important can be categorised into an epitype, or in a few cases more than one epidemiologically distinct epitype. Within each epitype, evidence available on the effects of climatic factors is provided for selected diseases of zoonotic importance to illustrate likely future evolution of these diseases and the extent of currently available evidence for different diseases. Factors which are likely to influence the emergence of novel zoonotic pathogens in Asia are outlined. The range of methods available for analysis, prediction, and evaluation of likely changes in disease occurrence under the influence of climate change has grown rapidly; an introduction is given to the types of tools now available. These methods will need to be integrated into a surveillance and response strategy for Asia, and an approach to achieve this is outlined.
The effects of rain and flooding on leptospirosis incidence in sheep and cattle in New Zealand
(Taylor and Francis Group on behalf of the New Zealand Veterinary Association, 2025-08-12) Sadler E; Vallee E; Watts J; Wada M
Aims To describe the spatio-temporal patterns of leptospirosis case counts in sheep and cattle in New Zealand, and to assess their association with climate variables indicative of flooding and surface runoff. As livestock are a major reservoir of Leptospira spp. and an important source of zoonotic transmission, understanding these patterns is critical for informing livestock and public health interventions in the context of climate change. Methods Confirmed cases of bovine and ovine leptospirosis from January 2011 to December 2023 were extracted from the Ministry for Primary Industries’ Animal Health Surveillance programme. Climate data was sourced from the National Institute of Water and Atmospheric Research. Using the χ2 test and Poisson regression models, the association between district-level case counts and four climate indices were examined: seasonal mean rainfall, seasonal frequency of extreme rainfall, seasonal mean soil moisture, and seasonal frequency of estimated surface runoff. Results Findings indicated an average of 13 confirmed cases for sheep annually, with notable surges in 2017 (34 cases) and 2023 (36 cases), aligning with extreme climate events. Poisson regression models for sheep leptospirosis identified significant associations with extreme rainfall (incidence risk ratio (IRR) = 5.03; 95% CI = 1.18–21.45), mean rainfall (IRR = 1.25; 95% CI = 1.15–1.36), surface runoff (IRR = 1.09; 95% CI = 1.04–1.15), and soil moisture (IRR = 1.03; 95% CI = 1.02–1.03). Cattle leptospirosis was positively associated with surface runoff (IRR = 1.06; 95% CI = 1.02–1.10) and soil moisture (IRR = 1.01; 95% CI = 1.00–1.01). Associations with extreme rainfall (IRR = 1.46; 95% CI = 0.49–4.31) and mean rainfall (IRR = 1.07; 95% CI = 1.00–1.14) were not statistically significant. Conclusions The outcomes of this study provide new evidence linking extreme rainfall, surface runoff, and other climate variables with increased leptospirosis case counts in sheep, with less pronounced but notable associations in cattle. These findings highlight the vulnerability of livestock to climate-driven disease pressures and suggest that future extreme weather events may increase the risk of leptospirosis outbreaks. This has important implications for targeted vaccination, surveillance, and public health preparedness in flood-prone rural regions of New Zealand.
The EpiCentre: redefining the future of animal health.
(American Veterinary Medical Association, 2024-11-07) Cogger N; Vallee E; Subharat S; Wada M; Sujau M; Han J-H; Isaksen KE; Compton CWR

Browsing by Author "Wada M"

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