Ferromagnetice exchange in bichloride bridged Cu(II) chains: magnetostructural correlations between ordered and disordered systems

The synthesis, structure, magnetic properties, and theoretical analysis of a new phase of dichloro(2-thlaro-3-methylpyridine)copper(II) (2) and its isomorphous analogue dichloro(2bromo-3-methylpyridine)copper(II) (3) are reported. Both complexes crystallize in the orthorhombic space group Pbca and present square pyramidal Cu(II) ions bridged into chains by chloride ions with each,copper(II)-bearing a single pyridine ligand. Variable temperature magnetic Susceptibility measurements were well fit by a uniform one-dimensional ferromagnetic chain model with,2) J = 69.0(7) K, C = 0.487 emu-K/mol-Oe; 3, J = 73.9(4) K, C = 0.463 emu-K/mol-Oe = Hamiltonian). The experimental J-values were confirmed-via theOretical calculations. Comparison to a known disordered polymorph of dichloro(2-chloro-3-inethylpyridine)copper(II), 1, shows marked differences as there are significant antiferromagnetic next-nearest neighbor interactions, in 1 in addition to randomness induced by the disorder which provide a distinctly different magnetic response. The differences in magnetic behavior are attributed principally to the structural difference in the Cu(II) coordination sphere, 1 being significantly closer to trigonal-bipyramidal, whose difference changes both the nearest and next-nearest neighbor interactions

A rapid and reproducible picture of open access health facility data in Africa to support the COVID-19 response

Background: Open data on the locations and services provided by health facilities in some countries have allowed the development of software tools contributing to COVID-19 response. The UN and WHO encourage countries to make health facility location data open, to encourage use and improvement. We provide a summary of open access health facility location data in Africa using re-useable code. We aim to support data analysts developing software tools to address COVID-19 response in individual countries. In Africa there are currently three main sources of such data; 1) direct from national ministries of health, 2) a database for sub-Saharan Africa collated and published by a team from KEMRI-Wellcome Trust Research Programme and now hosted by WHO, and 3) The Global Healthsites Mapping Project in collaboration with OpenStreetMap. Methods: We searched for and documented official national facility location data that were openly available. We developed re-useable open-source R code to summarise and visualise facility location data by country from the three sources. This re-useable code is used to provide a web user interface allowing data exploration through maps and plots of facility type. Results: Out of 53 African countries, seven provide an official open facility list that can be downloaded and analysed reproducibly. Considering all three sources, there are over 185,000 health facility locations available for Africa. However, there are differences and overlaps between sources and a lack of data on capacities and service provision. Conclusions: We suggest that these summaries and tools will encourage greater use of existing health facility location data, incentivise further improvements in the provision of those data by national suppliers, and encourage collaboration within wider data communities. The tools are a part of the afrimapr project, actively developing R building blocks to facilitate the use of health data in Africa

Using digital surveillance tools for near real-time mapping of the risk of infectious disease spread

Data from digital disease surveillance tools such as ProMED and HealthMap can complement the field surveillance during ongoing outbreaks. Our aim was to investigate the use of data collected through ProMED and HealthMap in real-time outbreak analysis. We developed a flexible statistical model to quantify spatial heterogeneity in the risk of spread of an outbreak and to forecast short term incidence trends. The model was applied retrospectively to data collected by ProMED and HealthMap during the 2013–2016 West African Ebola epidemic and for comparison, to WHO data. Using ProMED and HealthMap data, the model was able to robustly quantify the risk of disease spread 1–4 weeks in advance and for countries at risk of case importations, quantify where this risk comes from. Our study highlights that ProMED and HealthMap data could be used in real-time to quantify the spatial heterogeneity in risk of spread of an outbreak

Prioritizing COVID-19 vaccine allocation in resource poor settings: Towards an Artificial Intelligence-enabled and Geospatial-assisted decision support framework

Objectives: To propose a novel framework for COVID-19 vaccine allocation based on three components of Vulnerability, Vaccination, and Values (3Vs). Methods A combination of geospatial data analysis and artificial intelligence methods for evaluating vulnerability factors at the local level and allocate vaccines according to a dynamic mechanism for updating vulnerability and vaccine uptake. Results: A novel approach is introduced including (I) Vulnerability data collection (including country-specific data on demographic, socioeconomic, epidemiological, healthcare, and environmental factors), (II) Vaccination prioritization through estimation of a unique Vulnerability Index composed of a range of factors selected and weighed through an Artificial Intelligence (AIenabled) expert elicitation survey and scientific literature screening, and (III) Values consideration by identification of the most effective GIS assisted allocation of vaccines at the local level, considering context-specific constraints and objectives. Conclusions: We showcase the performance of the 3Vs strategy by comparing it to the actual vaccination rollout in Kenya. We show that under the current strategy, socially vulnerable individuals comprise only 45% of all vaccinated people in Kenya while if the 3Vs strategy was implemented, this group would be the first to receive vaccines

Real-time Epidemic Forecasting: Challenges and Opportunities.

Infectious disease outbreaks play an important role in global morbidity and mortality. Real-time epidemic forecasting provides an opportunity to predict geographic disease spread as well as case counts to better inform public health interventions when outbreaks occur. Challenges and recent advances in predictive modeling are discussed here. We identified data needs in the areas of epidemic surveillance, mobility, host and environmental susceptibility, pathogen transmissibility, population density, and healthcare capacity. Constraints in standardized case definitions and timely data sharing can limit the precision of predictive models. Resource-limited settings present particular challenges for accurate epidemic forecasting due to the lack of granular data available. Incorporating novel data streams into modeling efforts is an important consideration for the future as technology penetration continues to improve on a global level. Recent advances in machine-learning, increased collaboration between modelers, the use of stochastic semi-mechanistic models, real-time digital disease surveillance data, and open data sharing provide opportunities for refining forecasts for future epidemics. Epidemic forecasting using predictive modeling is an important tool for outbreak preparedness and response efforts. Despite the presence of some data gaps at present, opportunities and advancements in innovative data streams provide additional support for modeling future epidemics

Ferromagnetic Exchange in Bichloride Bridged Cu(II) Chains: Magnetostructural Correlations between Ordered and Disordered Systems

The synthesis, structure, magnetic properties, and theoretical analysis of a new phase of dichloro­(2-chloro-3-methylpyridine)­copper­(II) (<b>2</b>) and its isomorphous analogue dichloro­(2-bromo-3-methylpyridine)­copper­(II) (<b>3</b>) are reported. Both complexes crystallize in the orthorhombic space group <i>Pbca</i> and present square pyramidal Cu­(II) ions bridged into chains by chloride ions with each copper­(II) bearing a single pyridine ligand. Variable temperature magnetic susceptibility measurements were well fit by a uniform one-dimensional ferromagnetic chain model with <b>2</b>, <i>J</i> = 69.0(7) K, <i>C</i> = 0.487 emu-K/mol-Oe; <b>3</b>, <i>J</i> = 73.9(4) K, <i>C</i> = 0.463 emu-K/mol-Oe (<i>H</i> = −<i>J</i>Σ<i>S</i>_<i>i</i>·<i>S</i>_<i>j</i> Hamiltonian). The experimental <i>J</i>-values were confirmed via theoretical calculations. Comparison to a known disordered polymorph of dichloro­(2-chloro-3-methylpyridine)­copper­(II), <b>1</b>, shows marked differences as there are significant antiferromagnetic next-nearest neighbor interactions in <b>1</b> in addition to randomness induced by the disorder which provide a distinctly different magnetic response. The differences in magnetic behavior are attributed principally to the structural difference in the Cu­(II) coordination sphere, <b>1</b> being significantly closer to trigonal–bipyramidal, whose difference changes both the nearest and next-nearest neighbor interactions

Ferromagnetic Exchange in Bichloride Bridged Cu(II) Chains: Magnetostructural Correlations between Ordered and Disordered Systems

The synthesis, structure, magnetic properties, and theoretical analysis of a new phase of dichloro­(2-chloro-3-methylpyridine)­copper­(II) (<b>2</b>) and its isomorphous analogue dichloro­(2-bromo-3-methylpyridine)­copper­(II) (<b>3</b>) are reported. Both complexes crystallize in the orthorhombic space group <i>Pbca</i> and present square pyramidal Cu­(II) ions bridged into chains by chloride ions with each copper­(II) bearing a single pyridine ligand. Variable temperature magnetic susceptibility measurements were well fit by a uniform one-dimensional ferromagnetic chain model with <b>2</b>, <i>J</i> = 69.0(7) K, <i>C</i> = 0.487 emu-K/mol-Oe; <b>3</b>, <i>J</i> = 73.9(4) K, <i>C</i> = 0.463 emu-K/mol-Oe (<i>H</i> = −<i>J</i>Σ<i>S</i>_<i>i</i>·<i>S</i>_<i>j</i> Hamiltonian). The experimental <i>J</i>-values were confirmed via theoretical calculations. Comparison to a known disordered polymorph of dichloro­(2-chloro-3-methylpyridine)­copper­(II), <b>1</b>, shows marked differences as there are significant antiferromagnetic next-nearest neighbor interactions in <b>1</b> in addition to randomness induced by the disorder which provide a distinctly different magnetic response. The differences in magnetic behavior are attributed principally to the structural difference in the Cu­(II) coordination sphere, <b>1</b> being significantly closer to trigonal–bipyramidal, whose difference changes both the nearest and next-nearest neighbor interactions

Ferromagnetic Exchange in Bichloride Bridged Cu(II) Chains: Magnetostructural Correlations between Ordered and Disordered Systems

The synthesis, structure, magnetic properties, and theoretical analysis of a new phase of dichloro­(2-chloro-3-methylpyridine)­copper­(II) (<b>2</b>) and its isomorphous analogue dichloro­(2-bromo-3-methylpyridine)­copper­(II) (<b>3</b>) are reported. Both complexes crystallize in the orthorhombic space group <i>Pbca</i> and present square pyramidal Cu­(II) ions bridged into chains by chloride ions with each copper­(II) bearing a single pyridine ligand. Variable temperature magnetic susceptibility measurements were well fit by a uniform one-dimensional ferromagnetic chain model with <b>2</b>, <i>J</i> = 69.0(7) K, <i>C</i> = 0.487 emu-K/mol-Oe; <b>3</b>, <i>J</i> = 73.9(4) K, <i>C</i> = 0.463 emu-K/mol-Oe (<i>H</i> = −<i>J</i>Σ<i>S</i>_<i>i</i>·<i>S</i>_<i>j</i> Hamiltonian). The experimental <i>J</i>-values were confirmed via theoretical calculations. Comparison to a known disordered polymorph of dichloro­(2-chloro-3-methylpyridine)­copper­(II), <b>1</b>, shows marked differences as there are significant antiferromagnetic next-nearest neighbor interactions in <b>1</b> in addition to randomness induced by the disorder which provide a distinctly different magnetic response. The differences in magnetic behavior are attributed principally to the structural difference in the Cu­(II) coordination sphere, <b>1</b> being significantly closer to trigonal–bipyramidal, whose difference changes both the nearest and next-nearest neighbor interactions

Retrospective evaluation of real-time estimates of global COVID-19 transmission trends and mortality forecasts.

Since 8th March 2020 up to the time of writing, we have been producing near real-time weekly estimates of SARS-CoV-2 transmissibility and forecasts of deaths due to COVID-19 for all countries with evidence of sustained transmission, shared online. We also developed a novel heuristic to combine weekly estimates of transmissibility to produce forecasts over a 4-week horizon. Here we present a retrospective evaluation of the forecasts produced between 8th March to 29th November 2020 for 81 countries. We evaluated the robustness of the forecasts produced in real-time using relative error, coverage probability, and comparisons with null models. During the 39-week period covered by this study, both the short- and medium-term forecasts captured well the epidemic trajectory across different waves of COVID-19 infections with small relative errors over the forecast horizon. The model was well calibrated with 56.3% and 45.6% of the observations lying in the 50% Credible Interval in 1-week and 4-week ahead forecasts respectively. The retrospective evaluation of our models shows that simple transmission models calibrated using routine disease surveillance data can reliably capture the epidemic trajectory in multiple countries. The medium-term forecasts can be used in conjunction with the short-term forecasts of COVID-19 mortality as a useful planning tool as countries continue to relax public health measures