Large-scale Lassa fever outbreaks in Nigeria: quantifying the association between disease reproduction number and local rainfall

Deciphering the enigma of Lassa virus transmission dynamics and strategies for effective epidemic control through awareness campaigns and rodenticides

This study aims to formulate a mathematical framework to examine how the Lassa virus spreads in humans of opposite genders. The stability of the model is analyzed at an equilibrium point in the absence of the Lassa fever. The model's effectiveness is evaluated using real-life data, and all the parameters needed to determine the basic reproduction number are estimated. Sensitivity analysis is performed to pinpoint the crucial parameters significantly influencing the spread of the infection. The interaction between threshold parameters and the basic reproduction number is simulated. Control theory is employed to devise and evaluate strategies, such as awareness campaigns, advocating condom usage, and deploying rodenticides to reduce the possibility of virus transmission efficiently.

Modelling seasonality of Lassa fever incidences and vector dynamics in Nigeria

Lassa fever (Lf) is a viral haemorrhagic disease endemic to West Africa and is caused by the Lassa mammarenavirus. The rodent Mastomys natalensis serves as the primary reservoir and its ecology and behaviour have been linked to the distinct spatial and temporal patterns in the incidence of Lf. Nigeria has experienced an unprecedented epidemic that lasted from January until April of 2018, which has been followed by subsequent epidemics of Lf in the same period every year since. While previous research has modelled the case seasonality within Nigeria, this did not capture the seasonal variation in the reproduction of the zoonotic reservoir and its effect on case numbers. To this end, we introduce an approximate Bayesian computation scheme to fit our model to the case data from 2018-2020 supplied by the NCDC. In this study we used a periodically forced seasonal nonautonomous system of ordinary differential equations as a vector model to demonstrate that the population dynamics of the rodent reservoir may be responsible for the spikes in the number of observed cases in humans. The results show that in December through to March, spillover from the zoonotic reservoir drastically increases and spreads the virus to the people of Nigeria. Therefore to effectively combat Lf, attention and efforts should be concentrated during this period.

SERS Signature of SARS-CoV-2 in Saliva and Nasopharyngeal Swabs: Towards Perspective COVID-19 Point-of-Care Diagnostics

In this study, the intrinsic surface-enhanced Raman spectroscopy (SERS)-based approach coupled with chemometric analysis was adopted to establish the biochemical fingerprint of SARS-CoV-2 infected human fluids: saliva and nasopharyngeal swabs. The numerical methods, partial least squares discriminant analysis (PLS-DA) and support vector machine classification (SVMC), facilitated the spectroscopic identification of the viral-specific molecules, molecular changes, and distinct physiological signatures of pathetically altered fluids. Next, we developed the reliable classification model for fast identification and differentiation of negative CoV(-) and positive CoV(+) groups. The PLS-DA calibration model was described by a great statistical value-RMSEC and RMSECV below 0.3 and R²_cal at the level of ~0.7 for both type of body fluids. The calculated diagnostic parameters for SVMC and PLS-DA at the stage of preparation of calibration model and classification of external samples simulating real diagnostic conditions evinced high accuracy, sensitivity, and specificity for saliva specimens. Here, we outlined the significant role of neopterin as the biomarker in the prediction of COVID-19 infection from nasopharyngeal swab. We also observed the increased content of nucleic acids of DNA/RNA and proteins such as ferritin as well as specific immunoglobulins. The developed SERS for SARS-CoV-2 approach allows: (i) fast, simple and non-invasive collection of analyzed specimens; (ii) fast response with the time of analysis below 15 min, and (iii) sensitive and reliable SERS-based screening of COVID-19 disease.

Modeling the Lassa fever outbreak synchronously occurring with cholera and COVID-19 outbreaks in Nigeria 2021: A threat to Global Health Security

Nigeria struggles with seasonal outbreaks of Lassa fever (LF), with 70 to 100% of its states affected annually. Since 2018, the seasonal dynamics have changed with a stark increase in infections, though the pattern in 2021 differed from the other years. Nigeria had three outbreaks of Lassa Fever in 2021. In that year, Nigeria also experienced substantial burdens from COVID-19 and Cholera. There is potential that these three outbreak events interacted with each other. This may have been from community disruption and so changes in how people access the health system, how the health system responds, or overlapping biological interactions, misclassification, social factors, misinformation, and pre-existing disparities and vulnerabilities. We assessed the syndemic potential of Lassa Fever, COVID-19, and Cholera through modeling their interactions across the 2021 calendar year employing a Poisson regression model. We included the number of states affected and the month of the year. We used these predictors to forecast the progression of the outbreak using a Seasonal Autoregressive Integrated Moving Average (SARIMA) model. The Poisson model prediction for the confirmed number of Lassa fever cases was significantly dependent on the number of confirmed COVID-19 cases, the number of states affected, and the month of the year (p-value < 0.001), and the SARIMA model was a good fit, accounting for 48% of the change in the number of cases of Lassa fever (p-value < 0.001) with parameters ARIMA (6, 1, 3) (5, 0, 3). Lassa Fever, COVID-19, and Cholera 2021 case curves have mirrored dynamics and likely interact. Further research into common, intervenable aspects of those interactions should be performed.

Epidemiological trends of Lassa fever in Nigeria, 2018-2021

BACKGROUND

Lassa fever is a viral haemorrhagic fever endemic in Nigeria. Improved surveillance and testing capacity have revealed in an increased number of reported cases and apparent geographic spread of Lassa fever in Nigeria. We described the recent four-year trend of Lassa fever in Nigeria to improve understanding of its epidemiology and inform the design of appropriate interventions.

METHODS

We analysed the national surveillance data on Lassa fever maintained by the Nigeria Centre for Diseases Control (NCDC) and described trends, sociodemographic, geographic distribution, and clinical outcomes. We compared cases, positivity, and clinical outcomes in the period January 2018 to December 2021.

RESULTS

We found Lassa fever to be reported throughout the year with more than half the cases reported within the first quarter of the year, a recent increase in numbers and geographic spread of the virus, and male and adult (>18 years) preponderance. Case fatality rates were worse in males, the under-five and elderly, during off-peak periods, and among low reporting states.

CONCLUSION

Lassa fever is endemic in Nigeria with a recent increase in numbers and geographical distribution. Sustaining improved surveillance, enhanced laboratory diagnosis and improved case management capacity during off-peak periods should remain a priority. Attention should be paid to the very young and elderly during outbreaks. Further research efforts should identify and address specific factors that determine poor clinical outcomes.

Understanding the transmission pathways of Lassa fever: A mathematical modeling approach

The spread of Lassa fever infection is increasing in West Africa over the last decade. The impact of this can better be understood when considering the various possible transmission routes. We designed a mathematical model for the epidemiology of Lassa Fever using a system of nonlinear ordinary differential equations to determine the effect of transmission pathways toward the infection progression in humans and rodents including those usually neglected such as the environmental surface and aerosol routes. We analyzed the model and carried out numerical simulations to determine the impact of each transmission routes. Our results showed that the burden of Lassa fever infection is increased when all the transmission routes are incorporated and most single transmission routes are less harmful, but when in combination with other transmission routes, they increase the Lassa fever burden. It is therefore important to consider multiple transmission routes to better estimate the Lassa fever burden optimally and in turn determine control strategies targeted at the transmission pathways.

Unravelling the dynamics of Lassa fever transmission with differential infectivity: Modeling analysis and control strategies

Epidemic models have been broadly used to comprehend the dynamic behaviour of emerging and re-emerging infectious diseases, predict future trends, and assess intervention strategies. The symptomatic and asymptomatic features and environmental factors for Lassa fever (LF) transmission illustrate the need for sophisticated epidemic models to capture more vital dynamics and forecast trends of LF outbreaks within countries or sub-regions on various geographic scales. This study proposes a dynamic model to examine the transmission of LF infection, a deadly disease transmitted mainly by rodents through environment. We extend prior LF models by including an infectious stage to mild and severe as well as incorporating environmental contributions from infected humans and rodents. For model calibration and prediction, we show that the model fits well with the LF scenario in Nigeria and yields remarkable prediction results. Rigorous mathematical computation divulges that the model comprises two equilibria. That is disease-free equilibrium, which is locally-asymptotically stable (LAS) when the basic reproduction number, $ {\mathcal{R}}_{0} $, is $ < 1 $; and endemic equilibrium, which is globally-asymptotically stable (GAS) when $ {\mathcal{R}}_{0} $ is $ > 1 $. We use time-dependent control strategy by employing Pontryagin's Maximum Principle to derive conditions for optimal LF control. Furthermore, a partial rank correlation coefficient is adopted for the sensitivity analysis to obtain the model's top rank parameters requiring precise attention for efficacious LF prevention and control.

COVID-19 and Lassa fever in Nigeria: A deadly alliance?

As the COVID-19 pandemic poses serious threats to global public health, Nigeria faces a potential public health crisis owing to COVID-19 and other infectious diseases, such as Lassa fever (LF) and malaria. In this study, we discuss the possible determinants behind the decreased number of LF cases in Nigeria, which was likely due to the synergistic impact of the COVID-19 pandemic. During the COVID-19 pandemic, the epidemic curve of LF seems to have deviated from the general seasonal scale seen in past years, which could be due to underreporting of cases. In addition, partial compliance with nonpharmaceutical interventions, limited resources, or human behavior could be contributing factors. Thus, we suggest that better differentiation in terms of human and resource allocation between COVID-19 and LF could help curtail the transmission effectively.

Geographical drivers and climate-linked dynamics of Lassa fever in Nigeria

Lassa fever is a longstanding public health concern in West Africa. Recent molecular studies have confirmed the fundamental role of the rodent host (Mastomys natalensis) in driving human infections, but control and prevention efforts remain hampered by a limited baseline understanding of the disease's true incidence, geographical distribution and underlying drivers. Here, we show that Lassa fever occurrence and incidence is influenced by climate, poverty, agriculture and urbanisation factors. However, heterogeneous reporting processes and diagnostic laboratory access also appear to be important drivers of the patchy distribution of observed disease incidence. Using spatiotemporal predictive models we show that including climatic variability added retrospective predictive value over a baseline model (11% decrease in out-of-sample predictive error). However, predictions for 2020 show that a climate-driven model performs similarly overall to the baseline model. Overall, with ongoing improvements in surveillance there may be potential for forecasting Lassa fever incidence to inform health planning.

A seasonal model to assess intervention strategies for preventing periodic recurrence of Lassa fever

Lassa haemorrhagic fever is listed in WHO's Blueprint priority list of diseases and pathogens prioritized for research and development, affecting several hundreds of thousands of people each year. Lassa fever is spread via infected Natal multimammate mice and also through human-to-human contacts and it is a particular threat to pregnant women. Despite its importance, relatively few mathematical models have been established for modelling Lassa fever transmission up to now. We establish and study a new compartmental model for Lassa fever transmission including asymptomatic carriers, quarantine and periodic coefficients to model annual weather changes. We determine parameter values providing the best fit to data from Nigerian states Edo and Ondo from 2018–20. We perform uncertainty analysis and PRCC analysis to assess the importance of different parameters and numerical simulations to estimate the possible effects of control measures in eradicating the disease. The results suggest that the most important parameter which might be subject of control measures is death rate of mice, while mouse-to-human and human-to-human transmission rates also significantly influence the number of infected. However, decreasing the latter two parameters seems insufficient to eradicate the disease, while a parallel application of decreasing transmission rates and increasing mouse death rate might be able to stop the epidemic.

Infection pattern, case fatality rate and spread of Lassa virus in Nigeria

BACKGROUND

Lassa fever (LF) is a zoonotic infectious disease of public concern in Nigeria. The infection dynamics of the disease is not well elucidated in Nigeria. This study was carried out to describe the pattern of infection, case fatality rate and spread of lassa virus (LASV) from 2017 to 2020.

METHODS

Weekly epidemiological data on LF from December, 2016 to September, 2020 were obtained from Nigeria Centre for Disease Control. The number of confirmed cases and deaths were computed according to months and states. Descriptive statistics was performed and case fatality rate was calculated. Distribution and spread maps of LF over the four years period was performed on ArcMap 10.7.

RESULTS

A total of 2787 confirmed cases and 516 deaths were reported in Nigeria from December, 2016 to September, 2020. Increase in number of cases and deaths were observed with 298, 528, 796 and 1165 confirmed cases and 79, 125, 158 and 158 deaths in 2017, 2018, 2019 and 2020 respectively. Over 60% of the cases were reported in two states, Edo and Ondo states. The LF cases spread from 19 states in 2017 to 32 states and Federal Capital Territory (FCT) in 2020. Ondo state (25.39%) had the highest of deaths rate from LF over the four years. Case fatality rate (CFR) of LF was highest in 2017 (26.5%) with CFR of 23.7, 19.6 and 13.4% in 2018, 2019 and 2020 respectively. The peak of infection was in the month of February for the four years. Infections increases at the onset of dry season in November and decline till April when the wet season sets-in.

CONCLUSION

There is an annual increase in the number of LASV infection across the states in Nigeria. There is need to heighten control strategies through the use of integrated approach, ranging from vector control, health education and early diagnosis.

Forecasting the final disease size: comparing calibrations of Bertalanffy-Pütter models

Using monthly data from the Ebola-outbreak 2013-2016 in West Africa, we compared two calibrations for data fitting, least-squares (SSE) and weighted least-squares (SWSE) with weights reciprocal to the number of new infections. To compare (in hindsight) forecasts for the final disease size (the actual value was observed at month 28 of the outbreak) we fitted Bertalanffy-Pütter growth models to truncated initial data (first 11, 12, …, 28 months). The growth curves identified the epidemic peak at month 10 and the relative errors of the forecasts (asymptotic limits) were below 10%, if 16 or more month were used; for SWSE the relative errors were smaller than for SSE. However, the calibrations differed insofar as for SWSE there were good fitting models that forecasted reasonable upper and lower bounds, while SSE was biased, as the forecasts of good fitting models systematically underestimated the final disease size. Furthermore, for SSE the normal distribution hypothesis of the fit residuals was refuted, while the similar hypothesis for SWSE was not refuted. We therefore recommend considering SWSE for epidemic forecasts.

Predicting COVID-19 spread in the face of control measures in West Africa

The novel coronavirus (COVID-19) pandemic is causing devastating demographic, social, and economic damage globally. Understanding current patterns of the pandemic spread and forecasting its long-term trajectory is essential in guiding policies aimed at curtailing the pandemic. This is particularly important in regions with weak economies and fragile health care systems such as West Africa. We formulate and use a deterministic compartmental model to (i) assess the current patterns of COVID-19 spread in West Africa, (ii) evaluate the impact of currently implemented control measures, and (iii) predict the future course of the pandemic with and without currently implemented and additional control measures in West Africa. An analytical expression for the threshold level of control measures (involving a reduction in the effective contact rate) required to curtail the pandemic is computed. Considering currently applied health control measures, numerical simulations of the model using baseline parameter values estimated from West African COVID-19 data project a 67% reduction in the daily number of cases when the epidemic attains its peak. More reduction in the number of cases will be achieved if additional public health control measures that result in a reduction in the effective contact rate are implemented. We found out that disease elimination is difficult when more asymptomatic individuals contribute in transmission or are not identified and isolated in a timely manner. However, maintaining a baseline level of asymptomatic isolation and a low transmission rate will lead to a significant reduction in the number of daily cases when the pandemic peaks. For example, at the baseline level of asymptomatic isolation, at least a 46% reduction in the transmission rate is required for disease elimination. Additionally, disease elimination is possible if asymptomatic individuals are identified and isolated within 5 days (after the incubation period). Combining two or more measures is better for disease control, e.g., if asymptomatic cases are contact traced or identified and isolated in less than 8 days, only about 29% reduction in the disease transmission rate is required for disease elimination. Furthermore, we showed that the currently implemented measures triggered a 33% reduction in the time-dependent effective reproduction number between February 28 and June 26, 2020. We conclude that curtailing the COVID-19 pandemic burden significantly in West Africa requires more control measures than those that have already been implemented, as well as more mass testing and contact tracing in order to identify and isolate asymptomatic individuals early.

Modelling the Measles Outbreak at Hong Kong International Airport in 2019: A Data-Driven Analysis on the Effects of Timely Reporting and Public Awareness

Background

Measles, a highly contagious disease, still poses a huge burden worldwide. Lately, a trend of resurgence threatened the developed countries. A measles outbreak occurred in the Hong Kong International Airport (HKIA) between March and April 2019, which infected 29 airport staff. During the outbreak, multiple measures were taken including daily situation updates, setting up a public enquiry platform on March 23, and an emergent vaccination program targeting unprotected staff. The outbreak was put out promptly. The effectiveness of these measures was unclear.

Methods

We quantified the transmissibility of outbreak in HKIA by the effective reproduction number, R_eff(t), and basic reproduction number, R₀(t). The reproduction number was modelled as a function of its determinants that were statistically examined, including lags in hospitalization, situation update, and level of public awareness. Then, we considered a hypothetical no-measure scenario when improvements in reporting and public enquiry were absent and calculated the number of infected airport staff.

Results

Our estimated average R₀ is 10.09 (95% CI: 1.73−36.50). We found that R₀(t) was positively associated with lags in hospitalization and situation update, while negatively associated with the level of public awareness. The average predicted basic reproduction number, r₀, was 14.67 (95% CI: 9.01−45.32) under the no-measure scenario, which increased the average R₀ by 77.57% (95% CI: 1.71−111.15). The total number of infected staff would be 179 (IQR: 90−339, 95% CI: 23−821), namely the measure induced 8.42-fold (95% CI: 0.21−42.21) reduction in the total number of infected staff.

Conclusion

Timely reporting on outbreak situation and public awareness measured by the number of public enquiries helped to control the outbreak.

Mechanistic modelling of the large-scale Lassa fever epidemics in Nigeria from 2016 to 2019

Lassa fever, also known as Lassa hemorrhagic fever, is a virus that has generated recurrent outbreaks in West Africa. We use mechanistic modelling to study the Lassa fever epidemics in Nigeria from 2016-19. Our model describes the interaction between human and rodent populations with the consideration of quarantine, isolation and hospitalization processes. Our model supports the phenomenon of forward bifurcation where the stability between disease-free equilibrium and endemic equilibrium exchanges. Moreover, our model captures well the incidence curves from surveillance data. In particular, our model is able to reconstruct the periodic rodent and human forces of infection. Furthermore, we suggest that the three major epidemics from 2016-19 can be modelled by properly characterizing the rodent (or human) force of infection while the estimated human force of infection also present similar patterns across outbreaks. Our results suggest that the initial susceptibility likely increased across the three outbreaks from 2016-19. Our results highlight the similarity of the transmission dynamics driving three major Lassa fever outbreaks in the endemic areas.