Observed versus estimated actual trend of COVID-19 case numbers in Cameroon: A data-driven modelling.

Controlling the COVID-19 outbreak remains a challenge for Cameroon, as it is for many other countries worldwide. The number of confirmed cases reported by health authorities in Cameroon is based on observational data, which is not nationally representative. The actual extent of the outbreak from the time when the first case was reported in the country to now remains unclear. This study aimed to estimate and model the actual trend in the number of COVID -19 new infections in Cameroon from March 05, 2020 to May 31, 2021 based on an observed disaggregated dataset. We used a large disaggregated dataset, and multilevel regression and poststratification model was applied prospectively for COVID-19 cases trend estimation in Cameroon from March 05, 2020 to May 31, 2021. Subsequently, seasonal autoregressive integrated moving average (SARIMA) modeling was used for forecasting purposes. Based on the prospective MRP modeling findings, a total of about 7450935 (30%) of COVID-19 cases was estimated from March 05, 2020 to May 31, 2021 in Cameroon. Generally, the reported number of COVID-19 infection cases in Cameroon during this period underestimated the estimated actual number by about 94 times. The forecasting indicated a succession of two waves of the outbreak in the next two years following May 31, 2021. If no action is taken, there could be many waves of the outbreak in the future. To avoid such situations which could be a threat to global health, public health authorities should effectively monitor compliance with preventive measures in the population and implement strategies to increase vaccination coverage in the population.

Improving the management and security of COVID 19 diagnostic test data with a digital platform in resource-limited settings: The case of PlaCARD in Cameroon.

During the COVID 19 pandemic, round-the-clock demand for COVID -19 laboratory tests exceeded capacity, placing a significant burden on laboratory staff and infrastructure. The use of laboratory information management systems (LIMS) to streamline all phases of laboratory testing (preanalytical, analytical, and postanalytical) has become inevitable. The objective of this study is to describe the architecture, implementation, and requirements of PlaCARD, a software platform for managing patient registration, medical specimens, and diagnostic data flow, as well as reporting and authentication of diagnostic results during the 2019 coronavirus pandemic (COVID -19) in Cameroon. Building on its experience with biosurveillance, CPC developed an open-source, real-time digital health platform with web and mobile applications called PlaCARD to improve the efficiency and timing of disease-related interventions. PlaCARD was quickly adapted to the decentralization strategy of the COVID 19 testing in Cameroon and, after specific user training, was deployed in all COVID 19 diagnostic laboratories and the regional emergency operations center. Overall, 71% of samples tested for COVID 19 by molecular diagnostics in Cameroon from 05 March 2020 to 31 October 2021 were entered into PlaCARD. The median turnaround time for providing results was 2 days [0-2.3] before April 2021 and decreased to 1 day [1- 1] after the introduction of SMS result notification in PlaCARD. The integration of LIMS and workflow management into a single comprehensive software platform (PlaCARD) has strengthened COVID 19 surveillance capabilities in Cameroon. PlaCARD has demonstrated that it can be used as a LIMS for managing and securing test data during an outbreak.

Ground Level Deployment of Wireless Sensor Networks: Experiments, Evaluation and Engineering Insight.

In this paper, we are interested in characterizing the link properties of a wireless sensor network with nodes deployed at ground level. Such a deployment is fairly common in practice, e.g., when monitoring the vehicular traffic on a road segment or the status of infrastructures such as bridges, tunnels or dams. However, the behavior of off-the-shelf wireless sensor nodes in these settings is not yet completely understood. Through a thorough experimentation campaign, we evaluated not only the impact of the ground proximity on the wireless links, but also the impact of some parameters such as the packet payload, the communication channel frequency and the topography of the deployment area. Our results show that a ground-level deployment has a significant negative impact on the link quality, while parameters such as the packet size produce unexpected consequences. This allows us to parameter classical theoretical models in order to fit a ground-level deployment scenario. Finally, based on the lessons learned in our field tests, we discuss some considerations that must be taken into account during the design of communication protocols and before the sensor deployment in order to improve network performance.

The basic reproduction number for cellular SIR networks.

The basic reproduction number R0 is the average number of new infections produced by a typical infective individual in the early stage of an infectious disease, following the introduction of few infective individuals in a completely susceptible population. If R0 <1, then the disease dies, whereas for R0 >1 the infection can invade the host population and persist. This threshold quantity is well studied for SIR compartmental or mean field models based on ordinary differential equations, and a general method for its computation has been proposed by van den Driessche and Watmough. We concentrate here on SIR epidemiological models that take into account the contact network N underlying the transmission of the disease. In this context, it is generally admitted that R0 can be approximated by the average number R2,3 of infective individuals of generation three produced by an infective of generation two. We give here a simple analytic formula of R2,3 for SIR cellular networks. Simulations on two-dimensional cellular networks with von Neumann and Moore neighborhoods show that R2;3 can be used to capture a threshold phenomenon related the dynamics of SIR cellular network and confirm the good quality of the simple approach proposed recently by Aparicio and Pascual for the particular case of Moore neighborhood.

Low birth weight in perinatally HIV-exposed uninfected infants: observations in urban settings in Cameroon.

BACKGROUND: The consequences of maternal HIV infection for fetal growth are controversial. Here, we estimated the frequency of small for gestational age and gender (SGAG) among neonates born to HIV-infected or uninfected mothers and assessed the contribution, if any, of maternal HIV to the risk of SGAG. METHODS: The data used were obtained from the ANRS-Pediacam cohort in Cameroon. Pairs of newborns, one to a HIV-infected mother and the other to an uninfected mother, were identified during the first week of life, and matched on gender and recruitment site from 2007-2010. SGAG was defined in line with international recommendations as a birth weight Z-score adjusted for gestational age at delivery and gender more than two standard deviations below the mean (-2SD). Considering the matched design, logistic regression modeling was adjusted on site and gender to explore the effect of perinatal HIV exposure on SGAG. RESULTS: Among the 4104 mother-infant pairs originally enrolled, no data on birth weight and/or gestational age were available for 108; also, 259 were twins and were excluded. Of the remaining 3737 mother-infant pairs, the frequency of SGAG was 5.3% (95%CI: 4.6-6.0), and was significantly higher among HIV-infected infants (22.4% vs. 6.3%; p<.001) and lower among HIV-unexposed uninfected infants (3.5% vs. 6.3%; p<.001) than among HIV-exposed uninfected infants. Similarly, SGAG was significantly more frequent among HIV-infected infants (aOR: 4.1; 2.0-8.1) and less frequent among HIV-unexposed uninfected infants (aOR: 0.5; 0.4-0.8) than among HIV-exposed uninfected infants. Primiparity (aOR: 1.9; 1.3-2.7) and the presence of any disease during pregnancy (aOR: 1.4; 1.0-2.0) were identified as other contributors to SGAG. CONCLUSION: Maternal HIV infection was independently associated with SGAG for HIV-exposed uninfected infants. This provides further evidence of the need for adapted monitoring of pregnancy in HIV-infected women, especially if they are symptomatic, to minimize additional risk factors for SGAG.

Multiregional periodic matrix for modeling the population dynamics of sardine (Sardina pilchardus) along the moroccan atlantic coast: management elements for fisheries.

In this paper, we present a deterministic time discrete mathematical model based on multiregional periodic matrices to describe the dynamics of Sardina pilchardus in the Central Atlantic area of the Moroccan coast. This model deals with two stages (immature and mature) and three spatial zones where sardines are supposed to migrate from one zone to another. The population dynamics is described by an autonomous recurrence equation N(t + 1) = A.N(t), where A is a positive matrix whose entries are estimated using data collected during biannual acoustic surveys carried out from 2001 to 2003 onboard the Norwegian research vessel "Dr Fridtjof Nansen". The dominant eigenvalue lambda of A that gives the long-term growth rate of fish population is smaller than one. This agrees with the stock decrease observed in the data collected. We show that lambda is highly sensitive to the recruitment rate and much less sensitive to the reproduction rate. These results can clearly be used to define an efficient scenario in order to fight for instance against a stock decrease.

Effects of a disease affecting a predator on the dynamics of a predator-prey system.

We study the effects of a disease affecting a predator on the dynamics of a predator-prey system. We couple an SIRS model applied to the predator population, to a Lotka-Volterra model. The SIRS model describes the spread of the disease in a predator population subdivided into susceptible, infected and removed individuals. The Lotka-Volterra model describes the predator-prey interactions. We consider two time scales, a fast one for the disease and a comparatively slow one for predator-prey interactions and for predator mortality. We use the classical "aggregation method" in order to obtain a reduced equivalent model. We show that there are two possible asymptotic behaviors: either the predator population dies out and the prey tends to its carrying capacity, or the predator and prey coexist. In this latter case, the predator population tends either to a "disease-free" or to a "disease-endemic" state. Moreover, the total predator density in the disease-endemic state is greater than the predator density in the "disease-free" equilibrium (DFE).

The Ross-Macdonald model in a patchy environment.

We generalize to n patches the Ross-Macdonald model which describes the dynamics of malaria. We incorporate in our model the fact that some patches can be vector free. We assume that the hosts can migrate between patches, but not the vectors. The susceptible and infectious individuals have the same dispersal rate. We compute the basic reproduction ratio R(0). We prove that if R(0)1, then the disease-free equilibrium is globally asymptotically stable. When R(0)>1, we prove that there exists a unique endemic equilibrium, which is globally asymptotically stable on the biological domain minus the disease-free equilibrium.

Effect of the number of patches in a multi-patch SIRS model with fast migration on the basic reproduction rate.

We consider a two-patch epidemiological system where individuals can move from one patch to another, and local interactions between the individuals within a patch are governed by the classical SIRS model. When the time-scale associated with migration is much smaller than the time-scale associated with infection, aggregation methods can be used to simplify the initial complete model formulated as a system of ordinary differential equations. Analysis of the aggregated model then shows that the two-patch basic reproduction rate is smaller than the 1 patch one. We extend this result to a linear chain of P patches (P > 2). These results are illustrated by some examples for which numerical integration of the system of ordinary differential equations is performed. Simulations of an individual based model implemented with a multi-agent system are also carried out.