Previously published late blight forecasts which predict the threat of disease based on the presence or absence of favorable weather have not been effective in semi-arid potato-producing areas of the Pacific Northwest (Idaho, Oregon, and Washington). Research was conducted to identify weather variables useful for forecasting late blight in southern Idaho. The objectives of this research were to (i) determine if regional weather variables could be related to the occurrence of late blight in southern Idaho, (ii) determine if disease severity (scale of 0 to 4) could be predicted using variables found to be correlated with the annual occurrence of late blight, and (iii) validate the efficacy of this model in predicting disease incidence in regions of the Columbia Basin. Weather data were collected from five locations over a 9-year period (1995 to 2003). A binary logistic regression model (0 = no disease and 1 = disease) indicated that the number of hours with favorable conditions (10°C ≤ temperature ≤ 27°C, relative humidity ≥ 80%) in April and May (HF80m) was a significant disease predictor. Logistic regression analysis using an ordinal disease scale (0 = no disease and 4 = severe disease) indicated amount of precipitation (APj) and favorable hours (HF80j) with extended periods from April to June as significant disease predictors. The binary model predicted disease occurrence more accurately, with 67.5% accuracy (27/40 years correctly predicted), 75% sensitivity (12/16 late-blight years predicted), and 62.5% specificity (15/24 non-late-blight years predicted) using a leave-1-year-out error estimate. The binary model was validated with data (1995 to 2003) from the semi-arid Columbia Basin regions, predicting disease with 80.8% accuracy (21/26 years predicted), 84% sensitivity (21/25 outbreak years predicted), and 0% specificity (0/1 non-outbreak years predicted).

Increasing incidence has led to the re-appearance of pertussis as a public health problem in developed countries. Pertussis infection is usually mild in vaccinated children and adults, but it can be fatal in infants who are too young for effective vaccination (≤3 months). Tailoring of control strategies to prevent infection of the infant hinges on the availability of estimates of key epidemiological quantities. Here we estimate the serial interval of pertussis, i.e., the time between symptoms onset in a case and its infector, using data from a household-based study carried out in the Netherlands in 2007-2009. We use statistical methodology to tie infected persons to probable infector persons, and obtain statistically supported stratifications of the data by person-type (infant, mother, father, sibling). The analyses show that the mean serial interval is 20 days (95% CI: 16-23 days) when the mother is the infector of the infant, and 28 days (95% CI: 23-33 days) when the infector is the father or a sibling. These time frames offer opportunities for early mitigation of the consequences of infection of an infant once a case has been detected in a household. If preventive measures such as social distancing or antimicrobial treatment are taken promptly they could decrease the probability of infection of the infant.

Healthy Ageing (EIP on AHA) has been working since 2013 on the collection of good practices illustrating a variety of different approaches and incentives for integrated care in Europe. The catalogue of over 100 good practices has significantly contributed to a better understanding of the existing solutions, resources and expertise that can be pooled together towards the shared goals of the B3 Action Group which is to "replicate and scale-up integrated care in Europe". There is the hypothesis that sharing of experience of good practices should lead to their "easier and faster" adaptation and implementation in other regions.However, the challenge remains how to best leverage this existing body of evidence and utilise the good practice catalogue to make the learning embedded in the practices more readily and accessible to potential adopters. The good practices are often limited to a particular pilot, project or region but the ambitions of the EIP on AHA and B3 Action Group aims to promote the scaling up of these local innovative solutions across Europe. The challenge is to develop tools that can help us to understand how to stimulate changes towards more sustainable health and care systems, how to support implementation, scalability and transferability of good practices in Europe.The B3 Action Group intends to address this challenge b y providing regions with tools to support their efforts in the implementation of integrated care solutions. The B3 Maturity Model (B3-MM) is one example of such a tool. A key notion in the B3-MM is that of understanding the context in which a good practice has been developed, and into which a good practice will be transferred. The main goal of the B3-MM is to provide a multi-dimensional benchmark of the maturity of a context (the regional delivery system and political and organisational environment) in which a good practice operates or is proposed to transfer into.The B3-MM was developed by members of the B3 Action Group over 18 months, in collaboration with 12 EIP on AHA member regions, reflecting their efforts and ambitions in implementing integrated care at scale. A wide spectrum of stakeholders was involved in the development of the B3-MM, including national and regional decision-makers, health and care delivery

Purpose To ensure that more people will benefit from integrated care initiatives, scaling-up of successful initiatives is the way forward. However, new challenges present themselves as knowledge on how to achieve successful large-scale implementation is scarce. The EU-funded project SCIROCCO uses a step-based scaling-up strategy to explore what to scale-up, and how to scale-up integrated care initiatives by matching the complementary strengths and weaknesses of five European regions involved in integrated care. The purpose of this paper is to describe a multi-method evaluation protocol designed to understand what factors influence the implementation of the SCIROCCO strategy to support the scaling-up of integrated care. Design/methodology/approach The first part of the protocol focuses on the assessment of the implementation fidelity of the SCIROCCO step-based strategy. The objective is to gain insight in whether the step-based strategy is implemented as it was designed to explore what works and does not work when implementing the scaling-up strategy. The second part concerns a realist evaluation to examine what it is about the SCIROCCO’s strategy that works for whom, why, how and in which circumstances when scaling-up integrated care. Findings The intended study will provide valuable information on the implementation of the scaling-up strategy which will help to explain for what specific reasons the implementation succeeds and will facilitate further improvement of project outcomes. Originality/value The expected insights could be useful to guide the development, implementation and evaluation of future scaling-up strategies to advance the change towards more sustainable health and care systems.

Demographic events shape a population's genetic diversity, a process described by the coalescent-with-recombination (CwR) model that relates demography and genetics by an unobserved sequence of genealogies. The space of genealogies over genomes is large and complex, making inference under this model challenging.We approximate the CwR with a continuous-time and -space Markov jump process. We develop a particle filter for such processes, using waypoints to reduce the problem to the discrete-time case, and generalising the Auxiliary Particle Filter for discrete-time models. We use Variational Bayes for parameter inference to model the uncertainty in parameter estimates for rare events, avoiding biases seen with Expectation Maximization.Using real and simulated genomes, we show that past population sizes can be accurately inferred over a larger range of epochs than was previously possible, opening the possibility of jointly analyzing multiple genomes under complex demographic models.Code is available at https://github.com/luntergroup/smcsmc MSC 2010 subject classifications: Primary 60G55, 62M05, 62M20, 62F15; secondary 92D25.