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The variant of concern (VOC) P.1 emerged in the Amazonas state (Brazil) and was sequenced for the 1st time on 6-Jan-2021 by the Japanese National Institute of Infectious Diseases. It contains a constellation of mutations, ten of them in the spike protein. Consequences of these mutations at the populational level have been poorly studied so far. From December-2020 to February-2021, Manaus was devastated by four times more cases compared to the previous peak (April-2020). Here, data from the national health surveillance of hospitalized individuals were analysed using a model-based approach to estimate P.1 parameters of transmissibility and reinfection by maximum likelihood. Sensitivity analysis was performed changing pathogenicity and the period analysed (including/excluding the health system collapse period). In all analysed cases, the new variant transmissibility was found to be about 2.5 times higher compared to the previous variant in Manaus. A low probability of reinfection by the new variant (6.4%) was estimated, even under initial high prevalence (68%) by the time P.1 emerged. Consequences of a higher transmissibility were already observed with VOC B.1.1.7 in the UK and Europe. Urgent measures must be taken to control the spread of P.1.

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Citrus variegated chlorosis (CVC) and coffee leaf scorch (CLS) are two economically important diseases in Brazil caused by the bacterium Xylella fastidiosa. Strains of the bacterium isolated from the two plant hosts are very closely related, and the two diseases share sharpshooter insect vectors. In order to determine if citrus strains of X. fastidiosa could infect coffee and induce CLS disease, plant inoculations were performed. Plants of coffee, Coffea arabica ‘Mundo Novo’, grafted on Coffea canephora var. robusta ‘Apuatão 2258’ were mechanically inoculated with triply cloned strains of X. fastidiosa isolated from diseased coffee and citrus. Three months postinoculation, 5 of the 10 plants inoculated with CLS-X. fastidiosa and 1 of the 10 plants inoculated with CVC-X. fastidiosa gave positive enzyme-linked immunosorbent assay (ELISA) and/or polymerase chain reaction (PCR). Eight months postinoculation, another six plants inoculated with CVC-X. fastidiosa gave positive PCR results. The two X. fastidiosa strains were isolated from the inoculated plants and showed the same characteristics as the original clones by microscopy, ELISA, and PCR. None of the plants inoculated with sterile periwinkle wilt (PW) medium as controls gave positive reactions in diagnostic tests, and none developed disease symptoms. Six months postinoculation, seven plants inoculated with CLS-X. fastidiosa and eight inoculated with CVC-X. fastidiosa began to develop characteristic CLS symptoms, including apical and marginal leaf scorch, defoliation, and reductions of internode length, leaf size, and plant height, terminal clusters of small chlorotic and deformed leaves, and lateral shoot dieback. We have demonstrated that X. fastidiosa from citrus plants is pathogenic for coffee plants. This has important consequences for the management of CLS disease and has implications for the origin of citrus variegated chlorosis disease.

The SARS-CoV-2 pandemic has had an unprecedented impact on multiple levels of society. Not only has the pandemic completely overwhelmed some health systems but it has also changed how scientific evidence is shared and increased the pace at which such evidence is published and consumed, by scientists, policymakers and the wider public. More significantly, the pandemic has created tremendous challenges for decision-makers, who have had to implement highly disruptive containment measures with very little empirical scientific evidence to support their decision-making process. Given this lack of data, predictive mathematical models have played an increasingly prominent role. In high-income countries, there is a long-standing history of established research groups advising policymakers, whereas a general lack of translational capacity has meant that mathematical models frequently remain inaccessible to policymakers in low-income and middle-income countries. Here, we describe a participatory approach to modelling that aims to circumvent this gap. Our approach involved the creation of an international group of infectious disease modellers and other public health experts, which culminated in the establishment of the COVID-19 Modelling (CoMo) Consortium. Here, we describe how the consortium was formed, the way it functions, the mathematical model used and, crucially, the high degree of engagement fostered between CoMo Consortium members and their respective local policymakers and ministries of health.

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