Angus Cameron

A decision support tool to strategically prioritise evidence gathering in complex, hierarchical AND-OR decision trees. It is designed for situations with incomplete or uncertain information where the goal is to reach a confident conclusion as efficiently as possible (responding to the minimum number of questions, and only spending resources on generating improved evidence when it is of significant value to the final decision). The framework excels in complex analyses with multiple potential successful pathways to a conclusion ('OR' nodes). Key features include a dynamic influence index to guide users to the most impactful question, a system for propagating answers and semi-quantitative confidence scores (0-5) up the tree, and post-conclusion guidance to identify the best actions to increase the final confidence. These components are brought together in an interactive command-line workflow that guides the analysis from start to finish.

In Indonesia, the development of the poultry industry is facing numerous challenges. Major constraints include high disease burdens, large fluctuations in farm input and output prices, and inadequate biosecurity. Timely and reliable information about animal production and health can help stakeholders at all levels of the value chain make appropriate management decisions to optimize their profitability and productivity while reducing risks to public health. This study aimed to describe the challenges in the Indonesian poultry industry, assess stakeholders’ needs and capabilities in terms of generating and using poultry information for making production and health management decisions, and identify levers for improvement. Interviews were conducted with a diversity of key informants and value chain actors in five Indonesian provinces. Thematic analysis was applied with an interpretivist approach to gain an in-depth understanding of the lived experiences of various stakeholders and their opinions as to what might constitute appropriate solutions. Our findings indicate that market and political instability, ineffective management of poultry data, and limited inter-sectoral collaboration are limiting the development of the sector. Increased intersectoral cooperation is needed to implement standards for data collection and sharing across the industry, provide education and practical training on the use of information technologies for farm management, and accelerate research and innovation. Our study can contribute to the development of data-driven tools to support evidence-based decision-making at all levels of the poultry system.

Interventions to change antimicrobial use (AMU) practices can help mitigate the risk of antimicrobial resistance (AMR) development. However, changing AMU practices can be challenging due to the complex nature of the factors influencing AMU-related behaviours. This study used a qualitative approach to explore the factors that influenced decision-making on AMU by farmers and other actors in the Indonesian poultry sector. Thirty-five semi-structured interviews were conducted with farmers, technical services staff from the private sector, and representatives of associations, universities, and international organisations in Central Java, West Java, and East Java. Thematic analysis identified three patterns of influence on AMU: how farmers used information to make AMU-related decisions, the importance of farmers’ social and advisory networks, and the motivations driving changes in AMU behaviours. Key barriers identified included a lack of shared understanding around when to use antibiotics, financial pressures in the poultry sector, and a lack of engagement with government veterinary services. Potential opportunities identified included high farmer awareness of AMU, identification of private sector actors and peer networks as the stakeholders with established relationships of trust with farmers, and the importance of farmers’ conceptions of good farming practices, which could be engaged with to improve AMU practices.

African swine fever (ASF) is an infectious disease of swine causing major losses in the swine industry worldwide. Early detection of ASF is challenging because of the wide range of nonspecific clinical signs produced and its relatively low contagiousness. Monitoring pig mortality is a promising approach for early detection of ASF but such approach has been associated with delay in disease detection in large pig farms. The purpose of this study was to compare the effectiveness and suitability of early detection strategies for ASF in large commercial pig farms using mortality monitoring at the pen, room, or barn level. The within‐barn spread of the disease was modelled including the non‐homogeneous probabilities of transmission within pens, between pens and between rooms. The performances of early detection surveillance based on mortality thresholds established for different epidemiological units were compared in terms of sensitivity, time to detection, and number of false alarms per year. A barn with a capacity of 3200 pigs divided into 8 rooms with 10 pens each containing 40 pigs per pen was used as an example. Our results show that using room‐ or pen‐based mortality thresholds provided a time to detection of 8 days post disease introduction. Similar detection performances could be achieved with barn‐level mortality threshold but at the cost of an increased number of pigs to be tested each year. The different scenarios tested also show that barn characteristics such as baseline mortality rate and pen size had a limited impact on the pen‐level mortality thresholds required for disease early detection. These results offer strong support for using mortality data for early detection of ASF not only in small pig herds but also in large commercial barns. Furthermore, the mortality thresholds defined in this study might be relevant to a wide range of pig production sites.

Early detection surveillance is used for various purposes, including the early detection of non‐communicable diseases (e.g., cancer screening), of unusual increases of disease frequency (e.g., influenza or pertussis outbreaks), and the first occurrence of a disease in a previously free population. This latter purpose is particularly important due to the high consequences and cost of delayed detection of a disease moving to a new population. Quantifying the sensitivity of early detection surveillance allows important aspects of the performance of different systems, approaches and authorities to be evaluated, compared and improved. While quantitative evaluation of the sensitivity of other branches of surveillance have been available for many years, development has lagged in the area of early detection, arguably one of the most important purposes of surveillance. This paper, using mostly animal health examples, develops a simple approach to quantifying the sensitivity of early detection surveillance, in terms of population coverage, temporal coverage, and detection sensitivity. This approach is extended to quantify the benefits of risk‐based approaches to early detection surveillance. Population‐based clinical surveillance (based on either farmers and their veterinarians, or patients and their local health services) provides the best combination of sensitivity, practicality and cost‐effectiveness. These systems can be significantly enhanced by lifting disincentives to reporting, for instance by implementing effective strategies to improve farmer awareness and engagement with health services and addressing the challenges of well‐intentioned disease notification policies that inadvertently impose barriers to reporting.

Seasonal variation in COVID-19 incidence could impact the trajectory of the pandemic. Using global line-list data on COVID-19 cases reported until 29 ^th February 2020 and global gridded temperature data, and after adjusting for surveillance capacity and time since first imported case, higher average temperature was strongly associated with lower COVID-19 incidence for temperatures of 1°C and higher. However, temperature explained a relatively modest amount of the total variation in COVID-19 incidence. These preliminary findings support stringent containment efforts in Europe and elsewhere.

It has been said that epidemiology provides tools to help make good decisions in the face of uncertainty. The natural state of an epidemiologist is to be unsatisfied with the quality and quantity of data available. Biostatistics provides tools to quantify uncertainty and guide reliable decision making, while surveillance methodologies have evolved to increase our capacity to confidently make statements about the population of interest, while keeping costs to a minimum. The roots of the statistical and surveillance approaches extend back to nineteenth century and earlier, and some commonly used approaches were developed to address the practicalities of a pre-digital era. With the sudden availability of unparalleled communication and data management capabilities, many constraints in surveillance have been swept aside. However, there is a risk that we may continue to cling to outdated approaches designed to solve problems that no longer exist. This paper presents an alternative model for disease surveillance, which moves from current approaches which simply make the most of inadequate data. Instead, we contend that it is now possible to develop practical, cost-effective surveillance systems that generate high quality real time data covering the entire population and all disease and other relevant events. Should we therefore not actively work towards this new surveillance paradigm, and free ourselves from historical constraints? A vision for the future of epidemiology is also presented, where epidemiologists depend much less on the support of biostatisticians, and much more on social scientists and information and communication technology experts.