Time course of the four distinct HPAI epidemics considered.

<p>Number of infected farms detected daily for the four distinct epidemics. The data is given in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0000349#pone.0000349.s002" target="_blank">Table S1</a>.</p

Maximum likelihood estimates for the generation time distributions.

<p>Parameters of these distributions are shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0000349#pone-0000349-t001" target="_blank">Table 1</a>.</p

Estimates of the reproductive number over time for the four epidemics.

<p>The estimates are obtained with the MLE for the generation time distribution parameters. The light blue area shows the 95% confidence intervals. The vertical orange line marks the date of reinforced controls. For British Columbia, this was the date the decision to cull the HRR region was taken, for the other datasets it is the date of detection of HPAI within the area.</p

Maximum and mean reproductive numbers for each outbreak.

<p>Maximum reproductive numbers estimated for any farm during the course of each outbreak and mean reproductive numbers <i>R</i> (95% confidence intervals) prior to enforced interventions for the four different datasets. The mean reproductive numbers prior to intervention were calculated using <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0000349#pone.0000349.s011" target="_blank">Code S9</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0000349#pone.0000349.s012" target="_blank">Code S10</a>.</p

Estimating Air Temperature and Its Influence on Malaria Transmission across Africa

<div><p>Malaria transmission is strongly influenced by climatic conditions which determine the abundance and seasonal dynamics of the <i>Anopheles</i> vector. In particular, water temperature influences larval development rates whereas air temperature determines adult longevity as well as the rate of parasite development within the adult mosquito. Although data on land surface temperature exist at a spatial resolution of approximately 1 km globally with four time steps per day, comparable data are not currently available for air temperature. In order to address this gap and demonstrate the importance of using the right type of temperature data, we fitted simple models of the relationship between land-surface and air temperature at lower resolution to obtain a high resolution estimate of air temperature across Africa. We then used these estimates to calculate some crucial malaria transmission parameters that strongly depend on air temperatures. Our results demonstrate substantial differences between air and surface temperatures that impact temperature-based maps of areas suitable for transmission. We present high resolution maps of the malaria transmission parameters driven by air temperature and their seasonal variation. The fitted air temperature datasets are made publicly available alongside this publication.</p></div

Maps of the mean annual malaria transmission parameters.

<p>(A) average mosquito life span, (B) mean extrinsic incubation period (cut off at 60 days), (C) mean extrinsic infectious period, (D) mean biting rate, (E) mean number of infectious bites per infected mosquito and (F) temperature suitability index.</p

Parameters of the generation time distributions.

<p>Shown are the maximum likelihood estimates (95% confidence intervals) of the parameters <i>κ</i> and <i>η</i>, and of mean and variance of the resulting distributions.</p

Annual mean differences between observed surface and air temperature during night (left) and day (right).

<p>Locations L1, L2 and L3 at latitudes 19.5, 0, and −25.5 and longitudes 0. 19.5 and 25.5, respectively, give the locations for the time series shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0056487#pone-0056487-g002" target="_blank">Figure 2</a>.</p

Coefficients (95% CIs) of time series and location based variables, interactions between these as well as variances of the random effects for the model fitted to night time air temperatures.

<p>Coefficients (95% CIs) of time series and location based variables, interactions between these as well as variances of the random effects for the model fitted to night time air temperatures.</p

Decision tree modelling approach to malaria case management in the public sector.

<p>At the left-hand side the entry point is a febrile case seeking treatment. We next stratify on their true (unobserved) cause of fever as either malaria or non-malarial febrile illness (NMFI). The case management process then involves five steps – the availability of an RDT, whether the RDT is used, the outcome of the RDT given the true underlying cause of fever (based on the sensitivity and specificity of the diagnostic), whether an ACT is stock, and whether an ACT is prescribed given the RDT result or clinical diagnosis. This leads to four outcomes: correct treatment for malaria or for NMFI (shown as a green circle), under-treatment of malaria (shown as a purple circle), or overtreatment of an NMFI for malaria (shown as a red circle). In a perfect case management system there would be no under- or over-treatment.</p